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This file is MACHINE GENERATED! Do not edit. N) pywrap_tfe)context)core)execute)dtypes)annotation_types)op_def_registry)ops)op_def_library)deprecated_endpoints)dispatch) tf_export)TypeVarListAny) Annotated ApproxTopKvaluesindicesTV_ApproxTopK_Tz_atypes.BFloat16z_atypes.Float32z _atypes.Half approx_top_kTinputkreduction_dimension recall_targetis_max_kreduction_input_size_overrideaggregate_to_topkctjxstj}|j} | jr< t j |d||d|d|d|d|d|d|} t j| } | St||||||||fd} | tur| St3j4|d}|d }t3j4|d}|d }t3j6|d}|d}t3j8|d}|d }t3j4|d}|d}t3j8|d} t;j<d|||||||| \} } } }|dd} t3j>rd| jAdd| jAdd| jCdd| jEdd| jAdd| jEdd| jGdf}| jH}t3jJd||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||fd} | tur| St||||||||| S#tj $rYt"t$f$rNt'j(t*d t-|||||||| } | t&j.j0ur| cYSwxYw#t"t$f$rNt'j(t*d t-|||||||| } | t&j.j0ur| cYSwxYw)aReturns min/max k values and their indices of the input operand in an approximate manner. See https://arxiv.org/abs/2206.14286 for the algorithm details. This op is only optimized on TPU currently. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. Array to search. Must be at least 1-D of the floating type k: An `int` that is `>= 0`. Specifies the number of min/max-k. reduction_dimension: An optional `int`. Defaults to `-1`. Integer dimension along which to search. Default: -1. recall_target: An optional `float`. Defaults to `0.95`. Recall target for the approximation. Range in (0,1] is_max_k: An optional `bool`. Defaults to `True`. When true, computes max-k; otherwise computes min-k. reduction_input_size_override: An optional `int`. Defaults to `-1`. When set to a positive value, it overrides the size determined by `input[reduction_dim]` for evaluating the recall. This option is useful when the given `input` is only a subset of the overall computation in SPMD or distributed pipelines, where the true input size cannot be deferred by the `input` shape. aggregate_to_topk: An optional `bool`. Defaults to `True`. When true, aggregates approximate results to top-k. When false, returns the approximate results. The number of the approximate results is implementation defined and is greater equals to the specified `k`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (values, indices). values: A `Tensor`. Has the same type as `input`. indices: A `Tensor` of type `int32`. rrrrrrrN)rrrrrrnamectx)rrrrrrrr ffffff?TT)&_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_ApproxTopKOutput_make_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException_dispatcher_for_approx_top_kNotImplementedapprox_top_k_eager_fallback_SymbolicException TypeError ValueError _dispatchr rdict OpDispatcher NOT_SUPPORTED_executemake_int make_float make_bool_op_def_library_apply_op_helpermust_record_gradient _get_attr_intget_attr_get_attr_bool_get_attr_typeinputsrecord_gradient)rrrrrrrr _ctxtld_resulte__op_outputs_attrs _inputs_flats P/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_nn_ops.pyrrsJ    0h..0$ #\\  11 lD%a1F_mZ1%':,4A^5F(, . g  ..<< <   V Z    Du5H/<*2?\3D&* , Gi,,::: n   sP:H"2#L"I)5II)(I)-J JL5ALLAM1/M1zraw_ops.ApproxTopKc tj|d}|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d }tj|g|t j t jt jg\} \}|g} d|d|d|d|d|d |d | f} tjd d | | || } tjrtjd| | | tj| } | S)Nrr#rr$rTrrrr%s ApproxTopKrFattrsr!r r)r;r<r=r>args_to_matching_eager_dtypeshalfbfloat16float32rrArGr*r+) rrrrrrrr r!_attr_TrPrOrJs rQr3r3sp3!  ))*=?TUM%%m_E- H   * 5("*$&!"*"3"34QSr"s(():=*h!#@(#w 8&   ]Al#)s ?' ""$ lFG5  # #G ,' . TV_AvgPool_Tz_atypes.Float64NHWCvaluepadding data_formatreturnctjxstj}|j}|jr# t j |d||d|d|d|d| }|St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}t)j*d|||||| \} } } } | dd}t#j,r{d| j/dd| j/dd| j/dd| j/dd | j1d f }| j2}t#j4d||||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYwcc} wcc} w) awPerforms average pooling on the input. Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. Args: value: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[batch, height, width, channels]`. ksize: A list of `ints` that has length `>= 4`. The size of the sliding window for each dimension of `value`. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of `value`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `value`. AvgPoolksizestridesr`raNrerfr`rar r!Expected list for 'strides' argument to 'avg_pool' Op, not %r.r^)r_rerfr`rar r%)r&rr'r(rr)r,r-r.r/r0avg_pool_eager_fallbackr4 isinstancelisttupler5r;r<make_strr?r@rArCrErFrG)r_rerfr`rar rHrIrJrK_irLrMrNrOrPs rQavg_poolrpsj4    0h..0$ #\\ 11 iugui7M;8gn ED%= )  !#( ) **5: :b8  R ) :% : GdE] +  !#* + ,,9@ @"X  r9 - @' @   gy 1'K!!+}=+'88eWg*7!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L <2 (' .M  & &- ##At,,  # #    $ ugw!$88  # #    ; A<!G IIH".H  H"!H"&H88IIzraw_ops.AvgPoolc t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j|g|tjtjtjtjg\}\}|g} d|d|d|d|d|f } t jd d | | || } t jrt jd | | | | \} | Scc}wcc}w) Nrhrerirfr`r^rar%sAvgPoolrTrdrkrlrmr5r;r<rnrVrWrXrYrZfloat64rrArG) r_rerfr`rar r!ror[rPrOrJs rQrjrjs ED%= )  !#( ) **5: :b8  R ) :% : GdE] +  !#* + ,,9@ @"X  r9 - @' @   gy 1'K!!+}=+55ugsW\\SZScSceletetv}wFwFEIJ'8E, UIw 7c7 ,&   Z= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. AvgPool3Drerfr`raNrg>Expected list for 'ksize' argument to 'avg_pool3d' Op, not %r.@Expected list for 'strides' argument to 'avg_pool3d' Op, not %r.rxrrerfr`rar r%)r&rr'r(rr)r,r-r.r/r0avg_pool3d_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrrerfr`rar rHrIrJrKrorLrMrNrOrPs rQ avg_pool3dr8sk8    0h..0$ #\\ 11 k4 77M;8gn ED%= )  #%* + ,,5: :b8  R ) :% : GdE] +  #%, - ..9@ @"X  r9 - @' @   gy 1'K!!+}=+'885w$+DJ!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L \674 (' .M  & &- ##At,,  # #    & ugw!$88  # #    ; Arqzraw_ops.AvgPool3Dc t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j|g|tjtjtjtjg\}\}|g} d|d|d|d|d|f } t jd d | | || } t jrt jd | | | | \} | Scc}wcc}w) Nr{rer|rfr`rxrar%s AvgPool3DrsrTrzrt rrerfr`rar r!ror[rPrOrJs rQr~r~s ED%= )  #%* + ,,5: :b8  R ) :% : GdE] +  #%, - ..9@ @"X  r9 - @' @   gy 1'K!!+}=+55ugsW\\SZScSceletetv}wFwFEIJ'8E, UIw 7c7 ,&   \1\#)s ?' ""$ \674 (' .) ; ArvTV_AvgPool3DGrad_Torig_input_shapegradctjxstj}|j}|jr$ t j |d|||d|d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}t)j*d||||||| \} } } }|dd} t#j,r{d| j/dd| j/dd| j/dd| j/dd | j1d f }| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w) a"Computes gradients of average pooling function. Args: orig_input_shape: A `Tensor` of type `int32`. The original input dimensions. grad: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. Output backprop of shape `[batch, depth, rows, cols, channels]`. ksize: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `grad`. AvgPool3DGradrerfr`raNrgCExpected list for 'ksize' argument to 'avg_pool3d_grad' Op, not %r.EExpected list for 'strides' argument to 'avg_pool3d_grad' Op, not %r.rxrrrerfr`rar r%)r&rr'r(rr)r,r-r.r/r0avg_pool3d_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrrrerfr`rar rHrIrJrKrorLrMrNrOrPs rQavg_pool3d_gradrsv6    0h..0$ #\\ 11 ot%5tWe7Iw {Lgn ED%= )  (*/ 0 115: :b8  R ) :% : GdE] +  (*1 2 339@ @"X  r9 - @' @   gy 1'K!!+}=+'88*:$gw%0t=!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L vw8 (' .O  & &- ##At,,  # #    + Dw{4II  # #    ; A<"G IIH$0H  H$#H$(H;;IIzraw_ops.AvgPool3DGradc Ft|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j|g|tjtjtjtjg\} \}tj|tj}||g} d|d|d|d|d| f } t j d d | | || } t j"rt j$d | | | | \} | Scc}wcc}w) Nrrerrfr`rxrar%s AvgPool3DGradrsrTrrkrlrmr5r;r<rnrVrWrXrYrZrur.convert_to_tensorint32rrArG rrrerfr`rar r!ror[rPrOrJs rQrrs ED%= )  (*/ 0 115: :b8  R ) :% : GdE] +  (*1 2 339@ @"X  r9 - @' @   gy 1'K!!+}=+44dVS7<= 4`. The size of the sliding window for each dimension of the input. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `grad`. AvgPoolGradrerfr`raNrgAExpected list for 'ksize' argument to 'avg_pool_grad' Op, not %r.CExpected list for 'strides' argument to 'avg_pool_grad' Op, not %r.r^rr%)r&rr'r(rr)r,r-r.r/r0avg_pool_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrs rQ avg_pool_gradrsv4    0h..0$ #\\ 11 mT#3T7E7Iw {Lgn ED%= )  &(- . //5: :b8  R ) :% : GdE] +  &(/ 0 119@ @"X  r9 - @' @   gy 1'K!!+}=+'88(8t"GW#.T;!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L |VW6 (' .O  & &- ##At,,  # #    ) Dw{4II  # #    ; Arzraw_ops.AvgPoolGradc Ft|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j|g|tjtjtjtjg\} \}tj|tj}||g} d|d|d|d|d| f } t j d d | | || } t j"rt j$d | | | | \} | Scc}wcc}w) Nrrerrfr`r^rar%s AvgPoolGradrsrTrrrs rQrras ED%= )  &(- . //5: :b8  R ) :% : GdE] +  &(/ 0 119@ @"X  r9 - @' @   gy 1'K!!+}=+44dVS7<r?r@rArErCrDrFrG)rrrrrrrr rHrIrJrKrLrMrNrOrPs rQ%_batch_norm_with_global_normalizationrs@    0h..0$ #\\  11 0$1au,.I!#gn(()9;MN&001JLgh'88*a127=MF_15 7!QX QK' ""$3%%c*,>ll-.0K  !<=?F::L *L&'K (' .;  & &- ##At,,  # #    A Q41A$=D  # #   s0!D))E0<EE0/E04FFFz(raw_ops.BatchNormWithGlobalNormalizationc tj|d}tj|d}tj|||||g|tj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*tj,tj.g\} } | \}}}}}|||||g} d| d|d|f} tj0dd| | ||} tj2rtj4d| | | | \} | S)Nrrr%s BatchNormWithGlobalNormalizationrsrTr)r;r=r>rVrWrZruruint8int16int8 complex64int64qint8quint8qint32rYqint16quint16uint16 complex128rXuint32uint64rrArG)rrrrrrrr r!r[ _inputs_TrPrOrJs rQrrsT(()9;MN&001JLgh661au7MsU\UdUdfmfufuw~xExEGNGTGTV]VcVceleqeqszsDsDFMFSFSU\UbUbdkdrdrt{tBtBDKDTDTV]VdVdfmfufuw~wEwEGNGYGY[b[g[gipiwiwy@yGyGUJK'9$1aD%Q4', ,.>8 :&   @!$0C"& ('""$ *L&'K (' .r\$BatchNormWithGlobalNormalizationGrad)dxdmdvdbdg)TV_BatchNormWithGlobalNormalizationGrad_Tbackpropctjxstj}|j} | jr8 t j |d||||||d|d| } t j| } | Stj |d}tj"|d}t%j&d|||||||| \} } } }|dd} tj(rYd| j+dd| j-dd| j/df}| j0}tj2d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||| S#tj$rYMwxYw)aGradients for batch normalization. This op is deprecated. See `tf.nn.batch_normalization`. Args: t: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. A 4D input Tensor. m: A `Tensor`. Must have the same type as `t`. A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. v: A `Tensor`. Must have the same type as `t`. A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. gamma: A `Tensor`. Must have the same type as `t`. A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this Tensor will be multiplied with the normalized Tensor. backprop: A `Tensor`. Must have the same type as `t`. 4D backprop Tensor. variance_epsilon: A `float`. A small float number to avoid dividing by 0. scale_after_normalization: A `bool`. A bool indicating whether the resulted tensor needs to be multiplied with gamma. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (dx, dm, dv, db, dg). dx: A `Tensor`. Has the same type as `t`. dm: A `Tensor`. Has the same type as `t`. dv: A `Tensor`. Has the same type as `t`. db: A `Tensor`. Has the same type as `t`. dg: A `Tensor`. Has the same type as `t`. rrrNr)rrrrrrrr r%)r&rr'r(rr)+_BatchNormWithGlobalNormalizationGradOutputr+r,r-r.r/r08batch_norm_with_global_normalization_grad_eager_fallbackr4r;r=r>r?r@rArErCrDrFrG)rrrrrrrr rHrIrJrKrLrMrNrOrPs rQ)batch_norm_with_global_normalization_gradrsH    0h..0$ #\\  11 4dAq!U$&6#%>@g<AA'Jg n(()9;MN&001JLgh'88.!qAU9AAQJc59 ;!QX QK' ""$3%%c*,>ll-.0K  !<=?F::L . fgO 7 = =g F' .;  & &- ##At,,  # #    E Q5(5E$=D  # #   s06EF"E==FFF..GGz,raw_ops.BatchNormWithGlobalNormalizationGradc tj|d}tj|d}tj|||||g|tj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*tj,tj.g\} } | \}}}}}|||||g} d| d|d|f} tj0dd| | ||} tj2rtj4d| | | t6j9| } | S)Nrrr%s$BatchNormWithGlobalNormalizationGradrTr)r;r=r>rVrWrZrurrrrrrrrrrYrrrrrXrrrrArGrr+)rrrrrrrr r!r[rrPrOrJs rQrr3sb(()9;MN&001JLgh661a7QSVY`YhYhjqjyjy|C|I|IKRKXKXZaZgZgipiuiuw~wHwHJQJWJWY`YfYfhohvhvxxFxFHOHXHXZaZhZhjqjyjy{B{I{IKRK]K]_f_k_kmtm{m{}D}K}KYNO'9(1aE8Q5(+, ,.>8 :&   Da$0C"& ('""$ . fgO 7 = =g F' .r\ TV_BiasAdd_Tbiasc tjxstj}|j}|jr t j |d|||d|}|S|d}tj|d}tj d||||\}}} } | dd}tj"rHd| j%dd| j'df} | j(} tj*d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)aAdds `bias` to `value`. This is a special case of `tf.add` where `bias` is restricted to be 1-D. Broadcasting is supported, so `value` may have any number of dimensions. Args: value: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `qint16`, `quint16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`. Any number of dimensions. bias: A `Tensor`. Must have the same type as `value`. 1-D with size the last dimension of `value`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the bias tensor will be added to the last dimension of the value tensor. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. The tensor will be added to "in_channels", the third-to-the-last dimension. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `value`. BiasAddraNrar r!r^)r_rrar r%)r&rr'r(rr)r,r-r.r/r0bias_add_eager_fallbackr4r;rnr?r@rArErCrFrG) r_rrar rHrIrJrKrLrMrNrOrPs rQbias_addrGsv0    0h..0$ #\\ 11 iudM;Hg nK!!+}=+'88T{O!QX QK' ""$3%%c*Mll=)+F::L <2 (' ./  & &- ##At,,  # #    $ ;TtEE  # #   0C==ED++EEEE/.E/zraw_ops.BiasAddc`|d}tj|d}tj||g|tjtj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*tj,g\}}|\}}||g}d|d|f}tj.dd||||} tj0rtj2d||| | \} | S)Nr^rar%sBiasAddrsrTrr;rnrVrWrZrurrrrrrrrrrYrrrrrXrrrrArG) r_rrar r!r[rrPrOrJs rQrrsK!!+}=+66t}cGOO]d]l]lnun{n{~E~K~KMTMZMZ\c\h\hjqj{j{}D}J}JLSLYLY[b[i[ikrkyky{B{K{KMTM[M[]d]l]lnun|n|~E~P~PRYR^R^`g`n`npwp~p~LAB'9-5$, - 5&   ZD?zraw_ops.BiasAddV1c(tj||g|tjtjtj tj tjtjtjtjtjtjtjtjtjtj tj"tj$tj&tj(tj*g\}}|\}}||g}d|f}tj,dd||||}tj.rtj0d||||\}|S)Nr%s BiasAddV1rsrTr)r;rVrWrZrurrrrrrrrrrYrrrrrXrrrrArG) r_rr r!r[rrPrOrJs rQrrs66t}cGOO]d]l]lnun{n{~E~K~KMTMZMZ\c\h\hjqj{j{}D}J}JLSLYLY[b[i[ikrkyky{B{K{KMTM[M[]d]l]lnun|n|~E~P~PRYR^R^`g`n`npwp~p~LAB'9-5$, >&   \1\#)s ?' ""$ \674 (' .r\ TV_Conv_Tconv CHANNELS_LASTrsfilter batch_dimsgroupsc Ltjxstj} | j} | jr* t j | d| ||d|d|d|d|d|d|d|} | St|||||||||| f d } | tur| St/|t0t2fstd |z|Dcgc]}t5j6|d}}t5j8|d}|g}t/|t0t2fstd|z|Dcgc]}t5j6|d}}|d}t5j8|d}|g}t/|t0t2fstd|z|Dcgc]}t5j6|d}}|d}t5j6|d}|d}t5j6|d} t;j<d|||||||||| \}}}}|d d } t5j>rd|jAdd|jCdd|jCdd|jCdd|jCdd|jCdd|jEdd|jEdf}|jF}t5jHd||| | \} | S#t j$r } tj| | Yd } ~ nd } ~ wt j$rYnwxYw t|||||||||| f d } | tur| St|||||||||| | S#t j$rYtt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYwcc}wcc}wcc}w#tt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYw)a Computes a N-D convolution given (N+1+batch_dims)-D `input` and (N+2)-D `filter` tensors. General function for computing a N-D convolution. It is required that `1 <= N <= 3`. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`. Tensor of type T and shape `batch_shape + spatial_shape + [in_channels]` in the case that `channels_last_format = true` or shape `batch_shape + [in_channels] + spatial_shape` if `channels_last_format = false`. spatial_shape is N-dimensional with `N=2` or `N=3`. Also note that `batch_shape` is dictated by the parameter `batch_dims` and defaults to 1. filter: A `Tensor`. Must have the same type as `input`. An `(N+2)-D` Tensor with the same type as `input` and shape `spatial_filter_shape + [in_channels, out_channels]`, where spatial_filter_shape is N-dimensional with `N=2` or `N=3`. strides: A list of `ints`. 1-D tensor of length `N+2`. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[N+1] = 1`. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"CHANNELS_FIRST", "CHANNELS_LAST"`. Defaults to `"CHANNELS_LAST"`. Used to set the data format. By default `CHANNELS_FIRST`, uses `NHWC (2D) / NDHWC (3D)` or if `CHANNELS_LAST`, uses `NCHW (2D) / NCDHW (3D)`. dilations: An optional list of `ints`. Defaults to `[]`. 1-D tensor of length `N+2`. The dilation factor for each dimension of `input`. If set to `k > 1`, there will be `k-1` skipped cells between each filter element on that dimension. The dimension order is determined by the value of `channels_last_format`, see above for details. Dilations in the batch and depth dimensions must be 1. batch_dims: An optional `int`. Defaults to `1`. A positive integer specifying the number of batch dimensions for the input tensor. Should be less than the rank of the input tensor. groups: An optional `int`. Defaults to `1`. A positive integer specifying the number of groups in which the input is split along the channel axis. Each group is convolved separately with `filters / groups` filters. The output is the concatenation of all the groups results along the channel axis. Input channels and filters must both be divisible by groups. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Convrfr`explicit_paddingsra dilationsrrN) rfr`rrarrrr r!r") rrrfr`rrarrrr :Expected list for 'strides' argument to 'conv' Op, not %r.DExpected list for 'explicit_paddings' argument to 'conv' Op, not %r.r'2i&0d L g  ..<< <   , A_G Z    DuVW!((L>>N2AN20N2AP#!P#z raw_ops.Convc @t|ttfstd|z|D cgc]} t j | d}} t j |d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d}|g}t|ttfstd|z|D cgc]} t j | d }} |d }t j |d }|d }t j |d }t j||g| tjtjtjtjtjg\} } | \}}||g}d | d|d|d|d|d |d |d |f}t jdd ||| | }t jrt j d||||\}|Scc} wcc} wcc} w)Nrrfr`rrrrarrrsrrr%sConvrTr)rkrlrmr5r;r<rnrVrWrXrYrZrurrrArG)rrrfr`rrarrrr r!ror[rrPrOrJs rQrrsl GdE] +  & ' ((9@ @"X  r9 - @' @   gy 1' %e} 5  0 1 22M^^bx((-@A^^!K!!+}=+I Ie} -  ( ) **=FFbx  [1F)FJ  \:* ^ F   VX .&66vgll\c\l\lnun}n}@G@O@OQXQ^Q^Nab'9/5&, )Wi(- y, Hf F&   Wa F!$4 1' ""$  fg/ (' .K A_GsH H/H TV_Conv2D_Trsrsrsrsuse_cudnn_on_gpuc tjxstj} | j} | jr( t j | d|||d|d|d|d|d|d|} | St|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j(|d}|g}t|ttfst!d |z|D cgc]} t#j$| d}} |d }t#j&|d}|gd}t|ttfst!d|z|D cgc]} t#j$| d}} t+j,d||||||||| \}}}}|dd} t#j.rd|j1dd|j3dd|j5dd|j3dd|j3dd|j3dd|j3df}|j6}t#j8d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| S#t j$rYwxYwcc} wcc} wcc} w)a' Computes a 2-D convolution given 4-D `input` and `filter` tensors. Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following: 1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`. A 4-D tensor. The dimension order is interpreted according to the value of `data_format`, see below for details. filter: A `Tensor`. Must have the same type as `input`. A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]` strides: A list of `ints`. 1-D tensor of length 4. The stride of the sliding window for each dimension of `input`. The dimension order is determined by the value of `data_format`, see below for details. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv2Drfrr`rrarNrfrr`rrarr r!Expected list for 'dilations' argument to 'conv2d' Op, not %r.) rrrfr`rrrarr r%)r&rr'r(rr)r,r-r.r/r0conv2d_eager_fallbackr4rkrlrmr5r;r<rnr>r?r@rArErCrDrFrG)rrrfr`rrrarr rHrIrJrKrorLrMrNrOrPs rQconv2drs@z    0h..0$ #\\  11 heVY,i. {Y  g n GdE] +  !( ) **9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  !2 3 44M^^bx((-@A^^K!!+}=+I Ie} -  !* + ,,=FFbx  [1F)F'88fg!4D$5)YT K!QX QK' ""$3%%c*Ill9%'9  !34ill9%':ll./ll=);ll;' )F::L ,1 (' .q  & &- ##At,,  # #    " ;K->!YTtMM  # #    A_GsB&I0K* K/1K40J7JJ76J7;KK'&K'zraw_ops.Conv2Dc  t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }|gd }t|ttfstd |z|D cgc]} t j | d }} t j||g| tjtjtjtjtjg\} } | \}}||g} d | d|d|d|d|d |d |f}t jdd| || |}t j rt j"d| |||\}|Scc} wcc} wcc} w)Nrrfr`Trrrr^rarrrr%sConv2DrsrTrrkrlrmr5r;r<rnr>rVrWrXrYrZrurrrArG)rrrfr`rrrarr r!ror[rrPrOrJs rQrrysQ GdE] +  !( ) **9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  !2 3 44M^^bx((-@A^^K!!+}=+I Ie} -  !* + ,,=FFbx  [1F)F66vgll\c\l\lnun}n}@G@O@OQXQ^Q^Nab'9/5&, )W.@Iw(;]Ki I&   Y,f!$4 1' ""$ ,1 (' .E A_GsG7$G< HTV_Conv2DBackpropFilter_T filter_sizesc tjxstj} | j} | jr) t j | d| |||d|d|d|d|d|d|} | St|ttfst!d |z|Dcgc]}t#j$|d}}t#j&|d}|d }t#j(|d}|g}t|ttfst!d |z|Dcgc]}t#j$|d}}|d }t#j&|d}|gd}t|ttfst!d|z|Dcgc]}t#j$|d}}t+j,d||||||||||  \}}}}|dd} t#j.rd|j1dd|j3dd|j5dd|j3dd|j3dd|j3dd|j3df}|j6}t#j8d||| | \} | S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| | S#t j$rYwxYwcc}wcc}wcc}w)a Computes the gradients of convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[batch, in_height, in_width, in_channels]`. filter_sizes: A `Tensor` of type `int32`. An integer vector representing the tensor shape of `filter`, where `filter` is a 4-D `[filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv2DBackpropFilterrfrr`rrarNrLExpected list for 'strides' argument to 'conv2d_backprop_filter' Op, not %r.TVExpected list for 'explicit_paddings' argument to 'conv2d_backprop_filter' Op, not %r.r^rNExpected list for 'dilations' argument to 'conv2d_backprop_filter' Op, not %r.) rrrrfr`rrrarr r%)r&rr'r(rr)r,r-r.r/r0%conv2d_backprop_filter_eager_fallbackr4rkrlrmr5r;r<rnr>r?r@rArErCrDrFrG)rrrrfr`rrrarr rHrIrJrKrorLrMrNrOrPs rQconv2d_backprop_filterrsJT    0h..0$ #\\  11 $dE<7.0@)$&7[) -g n GdE] +  /18 9 ::9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  /1B C DDM^^bx((-@A^^K!!+}=+I Ie} -  /1: ; <<=FFbx  [1F)F'88e,-97(/1A2C,79%)+!QX QK' ""$3%%c*Ill9%'9  !34ill9%':ll./ll=);ll;' )F::L  fg? (' .y  & &- ##At,,  # #    2 |W+W-;Dd 44  # #    A_GB'I2K- K22K72J9J  J98J9=KK*)K*zraw_ops.Conv2DBackpropFilterc 8t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }|gd }t|ttfstd |z|D cgc]} t j | d }} t j||g| tjtjtjtjg\} } | \}}tj|tj }|||g}d | d|d|d|d|d |d |f}t j"dd||| | }t j$rt j&d||||\}|Scc} wcc} wcc} w)Nrrfr`Trrrr^rarrrr%sConv2DBackpropFilterrsrTr)rkrlrmr5r;r<rnr>rVrWrXrYrZrur.rrrrArG)rrrrfr`rrrarr r!ror[rrPrOrJs rQrrsf GdE] +  /18 9 ::9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  /1B C DDM^^bx((-@A^^K!!+}=+I Ie} -  /1: ; <<=FFbx  [1F)F66|7LcT[T`T`bibrbrt{uDuDFMFUFUTXY'9#5,'' gmmD,|4, )W.@Iw(;]Ki I&   4a #)s ?' ""$  fg? (' .G A_GsH $H HTV_Conv2DBackpropFilterV2_Tconv2d_backprop_filter_v2c tjxstj} | j} | jr) t j | d| |||d|d|d|d|d|d|} | St|||||||||| f d} | tur| St/|t0t2fstd |z|Dcgc]}t5j6|d}}t5j8|d}|d }t5j:|d}|g}t/|t0t2fstd|z|Dcgc]}t5j6|d}}|d}t5j8|d}|gd}t/|t0t2fstd|z|Dcgc]}t5j6|d}} t=j>d|||||||||| \}}}}|dd} t5j@rd|jCdd|jEdd|jGdd|jEdd|jEdd|jEdd|jEdf}|jH}t5jJd||| | \} | S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| f d} | tur| St|||||||||| | S#t j$rYtt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYwcc}wcc}wcc}w#tt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYw)aComputes the gradients of convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[batch, in_height, in_width, in_channels]`. filter: A `Tensor`. Must have the same type as `input`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. Only shape of tensor is used. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv2DBackpropFilterV2rfrr`rrarNrr" rrrrfr`rrrarr OExpected list for 'strides' argument to 'conv2d_backprop_filter_v2' Op, not %r.TYExpected list for 'explicit_paddings' argument to 'conv2d_backprop_filter_v2' Op, not %r.r^rQExpected list for 'dilations' argument to 'conv2d_backprop_filter_v2' Op, not %r.r%)&r&rr'r(rr)r,r-r.r/r0)_dispatcher_for_conv2d_backprop_filter_v2r2(conv2d_backprop_filter_v2_eager_fallbackr4r5r6r7r r r8r9r:rkrlrmr;r<rnr>r?r@rArErCrDrFrGrrrrfr`rrrarr rHrIrJrKrorLrMrNrOrPs rQr r GsX    0h..0$ #\\  11 &eV\7.0@)$&7[) -g nD8  gw8H; 4 :;?AGn$ n GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  24E F GGM^^bx((-@A^^K!!+}=+I Ie} -  24= > ??=FFbx  [1F)F ):: f/;W*13C4E.9,5DBAq#x, QK' ""$3%%c*Ill9%'9  !34ill9%':ll./ll=);ll;' )F::L  ,A (' .K  & &- ##At,,  # #    9 &,:J [)T <=ACg  & 5 w+W-;Dd 44  # #  z " "" %r4eF=I8?8?AQBSLN*ANNAO97O9zraw_ops.Conv2DBackpropFilterV2c t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }|gd }t|ttfstd |z|D cgc]} t j | d }} t j|||g| tjtjtjtjg\} } | \}}}|||g}d | d|d|d|d|d |d |f}t jdd||| | }t jrt j d||||\}|Scc} wcc} wcc} w)Nrrfr`Trrrr^rarrrr%sConv2DBackpropFilterV2rsrTr )rkrlrmr5r;r<rnr>rVrWrXrYrZrurrArGrrrrfr`rrrarr r!ror[rrPrOrJs rQrrs] GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  24E F GGM^^bx((-@A^^K!!+}=+I Ie} -  24= > ??=FFbx  [1F)F66v|7TVY\c\h\hjqjzjz}D}L}LNUN]N]\`a'9"+5&,., )W.@Iw(;]Ki I&   6$0C"& ('""$  ,A (' .G A_GsG+$G0 G5TV_Conv2DBackpropInput_T input_sizesc tjxstj} | j} | jr) t j | d| |||d|d|d|d|d|d|} | St|ttfst!d |z|Dcgc]}t#j$|d}}t#j&|d}|d }t#j(|d}|g}t|ttfst!d |z|Dcgc]}t#j$|d}}|d }t#j&|d}|gd}t|ttfst!d|z|Dcgc]}t#j$|d}}t+j,d||||||||||  \}}}}|dd} t#j.rd|j1dd|j3dd|j5dd|j3dd|j3dd|j3dd|j3df}|j6}t#j8d||| | \} | S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| | S#t j$rYwxYwcc}wcc}wcc}w)a Computes the gradients of convolution with respect to the input. Args: input_sizes: A `Tensor` of type `int32`. An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor. filter: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. out_backprop: A `Tensor`. Must have the same type as `filter`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `filter`. Conv2DBackpropInputrfrr`rrarNrKExpected list for 'strides' argument to 'conv2d_backprop_input' Op, not %r.TUExpected list for 'explicit_paddings' argument to 'conv2d_backprop_input' Op, not %r.r^rMExpected list for 'dilations' argument to 'conv2d_backprop_input' Op, not %r.) rrrrfr`rrrarr r%)r&rr'r(rr)r,r-r.r/r0$conv2d_backprop_input_eager_fallbackr4rkrlrmr5r;r<rnr>r?r@rArErCrDrFrG)rrrrfr`rrrarr rHrIrJrKrorLrMrNrOrPs rQconv2d_backprop_inputr sJT    0h..0$ #\\  11 #T; 7.0@)$&7[) -g n GdE] +  .07 8 999@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  .0A B CCM^^bx((-@A^^K!!+}=+I Ie} -  .09 : ;;=FFbx  [1F)F'88;v,8''.0@1B+6)$(*!QX QK' ""$3%%c*Ill9%'9  !34ill9%':ll./ll=);ll;' )F::L |VW> (' .y  & &- ##At,,  # #    1 v|W+W-;Dd 44  # #    A_Grzraw_ops.Conv2DBackpropInputc Vt|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }|gd }t|ttfstd |z|D cgc]} t j | d }} t j||g| tjtjtjtjtjg\} } | \}}tj |tj}|||g}d | d|d|d|d|d |d |f}t j"dd||| | }t j$rt j&d||||\}|Scc} wcc} wcc} w)Nrrfr`Trrrr^rarrrr%sConv2DBackpropInputrsrTr)rkrlrmr5r;r<rnr>rVrWrXrYrZrurr.rrrArG)rrrrfr`rrrarr r!ror[rrPrOrJs rQrrs{ GdE] +  .07 8 999@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  .0A B CCM^^bx((-@A^^K!!+}=+I Ie} -  .09 : ;;=FFbx  [1F)F66 7MsU\UaUacjcscsu|vEvEGNGVGVX_XeXeUhi'9$6<&&{GMMB+v|4, )W.@Iw(;]Ki I&   3Q|#)s ?' ""$ |VW> (' .G A_GsH$H! H&TV_Conv2DBackpropInputV2_Tconv2d_backprop_input_v2c tjxstj} | j} | jr) t j | d| |||d|d|d|d|d|d|} | St|||||||||| f d} | tur| St/|t0t2fstd |z|Dcgc]}t5j6|d}}t5j8|d}|d }t5j:|d}|g}t/|t0t2fstd|z|Dcgc]}t5j6|d}}|d}t5j8|d}|gd}t/|t0t2fstd|z|Dcgc]}t5j6|d}} t=j>d|||||||||| \}}}}|dd} t5j@rd|jCdd|jEdd|jGdd|jEdd|jEdd|jEdd|jEdf}|jH}t5jJd||| | \} | S#t j$r } tj| | Yd} ~ nd} ~ wt j$rYnwxYw t|||||||||| f d} | tur| St|||||||||| | S#t j$rYtt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYwcc}wcc}wcc}w#tt f$rPt#j$t&d t)|||||||||| } | t"j*j,ur| cYSwxYw)aComputes the gradients of convolution with respect to the input. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`. 4-D with shape `[batch, in_height, in_width, in_channels]`. Only shape of tensor is used. filter: A `Tensor`. Must have the same type as `input`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. explicit_paddings: An optional list of `ints`. Defaults to `[]`. If `padding` is `"EXPLICIT"`, the list of explicit padding amounts. For the ith dimension, the amount of padding inserted before and after the dimension is `explicit_paddings[2 * i]` and `explicit_paddings[2 * i + 1]`, respectively. If `padding` is not `"EXPLICIT"`, `explicit_paddings` must be empty. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv2DBackpropInputV2rfrr`rrarNrr"rNExpected list for 'strides' argument to 'conv2d_backprop_input_v2' Op, not %r.TXExpected list for 'explicit_paddings' argument to 'conv2d_backprop_input_v2' Op, not %r.r^rPExpected list for 'dilations' argument to 'conv2d_backprop_input_v2' Op, not %r.r%)&r&rr'r(rr)r,r-r.r/r0(_dispatcher_for_conv2d_backprop_input_v2r2'conv2d_backprop_input_v2_eager_fallbackr4r5r6r7r r#r8r9r:rkrlrmr;r<rnr>r?r@rArErCrDrFrGrs rQr#r#sX    0h..0$ #\\  11 %tUFL7.0@)$&7[) -g nB7  gw8H; 4 :;?AGn$ n GdE] +  13: ; <<9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  13D E FFM^^bx((-@A^^K!!+}=+I Ie} -  13< = >>=FFbx  [1F)F )::uV.:G)02B3D-8I&*,Aq#x* QK' ""$3%%c*Ill9%'9  !34ill9%':ll./ll=);ll;' )F::L vw@ (' .G  & &- ##At,,  # #    8 &,:J [)T <=ACg  & 4 w+W-;Dd 44  # #  z "  "" $b$U67>@PAR;F9B+O  g  ..<< <   2 A_G Z    "B5:F5N?P9D7@t )M Gi,,::: n   rzraw_ops.Conv2DBackpropInputV2c t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }|gd }t|ttfstd |z|D cgc]} t j | d }} t j|||g| tjtjtjtjtjg\} } | \}}}|||g}d | d|d|d|d|d |d |f}t jdd||| | }t j rt j"d||||\}|Scc} wcc} wcc} w)Nr&rfr`Trr'rr^rarr(rr%sConv2DBackpropInputV2rsrTr%rrs rQr*r*Vso GdE] +  13: ; <<9@ @"X  r9 - @' @   gy 1'''(8:LM %e} 5  13D E FFM^^bx((-@A^^K!!+}=+I Ie} -  13< = >>=FFbx  [1F)F66v|7TVY\c\h\hjqjzjz}D}L}LNUN]N]_f_l_l\op'9"+5&,., )W.@Iw(;]Ki I&   5q#)s ?' ""$ vw@ (' .E A_GsG:$G? H TV_Conv3D_Trsrsrsrsrsctjxstj}|j}|jr$ t j |d|||d|d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}|gd }t|ttfst!d |z|D cgc]} t#j$| d}} t)j*d||||||| \} } } }|dd} t#j,r{d | j/d d| j1dd| j1dd| j1dd| j1df }| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w)aComputes a 3-D convolution given 5-D `input` and `filter` tensors. In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product. Our Conv3D implements a form of cross-correlation. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. Shape `[batch, in_depth, in_height, in_width, in_channels]`. filter: A `Tensor`. Must have the same type as `input`. Shape `[filter_depth, filter_height, filter_width, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1, 1]`. 1-D tensor of length 5. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv3Drfr`rarNrfr`rarr r!Expected list for 'dilations' argument to 'conv3d' Op, not %r.)rrrfr`rarr r%)r&rr'r(rr)r,r-r.r/r0conv3d_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrfr`rarr rHrIrJrKrorLrMrNrOrPs rQconv3dr4sJ    0h..0$ #\\ 11 heVY [)Egn GdE] +  !( ) **9@ @"X  r9 - @' @   gy 1'K!!+}=+I Ie} -  !* + ,,=FFbx  [1F)F'88fg!{%D2!QX QK' ""$3%%c*Ill9%y#,,y2IS\\-8+ll;')F::L ,1 (' .S  & &- ##At,,  # #    " '!YTtMM  # #    AGs<"G# II #H*6HH*)H*.IIIzraw_ops.Conv3Dc t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}|gd}t|ttfstd|z|Dcgc]}t j |d}}t j||g|tjtjtjtjg\} } | \}}||g} d | d|d|d|d|f } t jd d | | || } t jrt jd | | | | \} | Scc}wcc}w)Nr1rfr`rxrar-r2rr%sConv3DrsrTr/rt)rrrfr`rarr r!ror[rrPrOrJs rQr3r3s GdE] +  !( ) **9@ @"X  r9 - @' @   gy 1'K!!+}=+I Ie} -  !* + ,,=FFbx  [1F)F66vgll\c\l\lnun}n}@G@O@ONRS'9/5&, )Wik9 6&   Y,f!$4 1' ""$ ,1 (' ./ AGs E?&FTV_Conv3DBackpropFilter_Tcrtjxstj}|j}|jr# t j |d||||d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}|gd}t|ttfst!d |z|D cgc]} t#j$| d}} t)j*d||||||| \} } } }|dd} t#j,rjd | j/d d| j1dd| j1dd| j1df}| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w) a Computes the gradients of 3-D convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. Shape `[batch, depth, rows, cols, in_channels]`. filter: A `Tensor`. Must have the same type as `input`. Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: A `Tensor`. Must have the same type as `input`. Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1, 1]`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv3DBackpropFilterrfr`rNrfr`rr r!LExpected list for 'strides' argument to 'conv3d_backprop_filter' Op, not %r.r-NExpected list for 'dilations' argument to 'conv3d_backprop_filter' Op, not %r.rrrrfr`rr r%)r&rr'r(rr)r,r-r.r/r0%conv3d_backprop_filter_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGrrrrfr`rr rHrIrJrKrorLrMrNrOrPs rQconv3d_backprop_filterr?sT.    0h..0$ #\\ 11 $dE6<7Iw YHgn GdE] +  /18 9 ::9@ @"X  r9 - @' @   gy 1'I Ie} -  /1: ; <<=FFbx  [1F)F'88eF-97(/9%) +!QX QK' ""$3%%c*Ill9%y#,,y2I3<< 46F::L  fg? (' .M  & &- ##At,,  # #    2 wDd44  # #    AG<!F7 H/-H47G> G%%G>=G>HH,+H,zraw_ops.Conv3DBackpropFilterct|ttfstd|z|Dcgc]}t j |d}}t j |d}|gd}t|ttfstd|z|Dcgc]}t j |d}}t j|||g|tjtjtjg\} } | \}}}|||g} d| d|d|d|f} t jdd | | || } t jrt jd | | | | \} | Scc}wcc}w) Nr:rfr`r-r;rr%sConv3DBackpropFilterrsrTr8rkrlrmr5r;r<rnrVrWrXrZrurrArGrrrrfr`rr r!ror[rrPrOrJs rQr=r=H s GdE] +  /18 9 ::9@ @"X  r9 - @' @   gy 1'I Ie} -  /1: ; <<=FFbx  [1F)F66v|7TVY\c\h\hjqjyjy|C|K|K\NO'9"+5&,., )Wi+  &   4a #)s ?' ""$  fg? (' .) AG E ETV_Conv3DBackpropFilterV2_Tznn.conv3d_backprop_filterznn.conv3d_backprop_filter_v2)v1ctjxstj}|j} | jr% t j |d||||d|d|d|d|} | St||||||||fd} | tur| St/|t0t2fstd |z|D cgc]} t5j6| d}} t5j8|d}|d }t5j8|d}|gd }t/|t0t2fstd |z|D cgc]} t5j6| d}} t;j<d|||||||| \} } }}|dd} t5j>r{d|jAdd|jCdd|jCdd|jCdd|jCdf }|jD}t5jFd||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||fd} | tur| St||||||||| S#t j$rYtt f$rNt#j$t&dt)|||||||| } | t"j*j,ur| cYSwxYwcc} wcc} w#tt f$rNt#j$t&dt)|||||||| } | t"j*j,ur| cYSwxYw)aEComputes the gradients of 3-D convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. Shape `[batch, depth, rows, cols, in_channels]`. filter_sizes: A `Tensor` of type `int32`. An integer vector representing the tensor shape of `filter`, where `filter` is a 5-D `[filter_depth, filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: A `Tensor`. Must have the same type as `input`. Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1, 1]`. 1-D tensor of length 5. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv3DBackpropFilterV2rfr`rarNr0r")rrrrfr`rarr OExpected list for 'strides' argument to 'conv3d_backprop_filter_v2' Op, not %r.rxr-QExpected list for 'dilations' argument to 'conv3d_backprop_filter_v2' Op, not %r.r%)$r&rr'r(rr)r,r-r.r/r0)_dispatcher_for_conv3d_backprop_filter_v2r2(conv3d_backprop_filter_v2_eager_fallbackr4r5r6r7r conv3d_backprop_filter_v2r8r9r:rkrlrmr;r<rnr?r@rArErCrFrG)rrrrfr`rarr rHrIrJrKrorLrMrNrOrPs rQrMrMf sP    0h..0$ #\\  11 &e\i)Wm[)-gn>8  lGWk4 !Gn$ n GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1'K!!+}=+I Ie} -  24= > ??=FFbx  [1F)F ):: L/;W*1{,5D BAq#x$ QK' ""$3%%c*Ill9%y#,,y2IS\\-8+ll;')F::L  ,A (' .c  & &- ##At,,  # #    9 , gw T !#g  & 5 |Wg!YTtMM  # #  z "  "" %r4e=I=I8?8? ??=FFbx  [1F)F66|7LcT[T`T`bibrbrt{uDuDFMFUFUTXY'9#5,'' gmmD,|4, )Wik9 6&   6$0C"& ('""$  ,A (' .3 AGs F$&F)TV_Conv3DBackpropInput_Tcrtjxstj}|j}|jr# t j |d||||d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}|gd}t|ttfst!d |z|D cgc]} t#j$| d}} t)j*d||||||| \} } } }|dd} t#j,rjd | j/d d| j1dd| j1dd| j1df}| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w) a Computes the gradients of 3-D convolution with respect to the input. Args: input: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. Shape `[batch, depth, rows, cols, in_channels]`. filter: A `Tensor`. Must have the same type as `input`. Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: A `Tensor`. Must have the same type as `input`. Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1, 1]`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Conv3DBackpropInputrfr`rNr9KExpected list for 'strides' argument to 'conv3d_backprop_input' Op, not %r.r-MExpected list for 'dilations' argument to 'conv3d_backprop_input' Op, not %r.r<r%)r&rr'r(rr)r,r-r.r/r0$conv3d_backprop_input_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGr>s rQconv3d_backprop_inputrU sT.    0h..0$ #\\ 11 #T5&,7Iw YHgn GdE] +  .07 8 999@ @"X  r9 - @' @   gy 1'I Ie} -  .09 : ;;=FFbx  [1F)F'88U6,8''.)$( *!QX QK' ""$3%%c*Ill9%y#,,y2I3<< 46F::L |VW> (' .M  & &- ##At,,  # #    1 wDd44  # #    AGr@zraw_ops.Conv3DBackpropInputct|ttfstd|z|Dcgc]}t j |d}}t j |d}|gd}t|ttfstd|z|Dcgc]}t j |d}}t j|||g|tjtjtjg\} } | \}}}|||g} d| d|d|d|f} t jdd | | || } t jrt jd | | | | \} | Scc}wcc}w) NrRrfr`r-rSrr%sConv3DBackpropInputrsrTrQrBrCs rQrTrT[ s GdE] +  .07 8 999@ @"X  r9 - @' @   gy 1'I Ie} -  .09 : ;;=FFbx  [1F)F66v|7TVY\c\h\hjqjyjy|C|K|K\NO'9"+5&,., )Wi+  &   3Q|#)s ?' ""$ |VW> (' .) AGrDTV_Conv3DBackpropInputV2_TTV_Conv3DBackpropInputV2_Tshapectjxstj}|j} | jr% t j |d||||d|d|d|d|} | St|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}|gd }t|ttfst!d |z|D cgc]} t#j$| d}} t)j*d|||||||| \} } }}|dd} t#j,rd |j/d d|j1dd|j1dd|j1dd|j1dd|j/df }|j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYwcc} wcc} w)aComputes the gradients of 3-D convolution with respect to the input. Args: input_sizes: A `Tensor`. Must be one of the following types: `int32`, `int64`. An integer vector representing the tensor shape of `input`, where `input` is a 5-D `[batch, depth, rows, cols, in_channels]` tensor. filter: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. Shape `[depth, rows, cols, in_channels, out_channels]`. `in_channels` must match between `input` and `filter`. out_backprop: A `Tensor`. Must have the same type as `filter`. Backprop signal of shape `[batch, out_depth, out_rows, out_cols, out_channels]`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1, 1]`. 1-D tensor of length 5. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `filter`. Conv3DBackpropInputV2rfr`rarNr0NExpected list for 'strides' argument to 'conv3d_backprop_input_v2' Op, not %r.rxr-PExpected list for 'dilations' argument to 'conv3d_backprop_input_v2' Op, not %r.)rrrrfr`rarr r%Tshape)r&rr'r(rr)r,r-r.r/r0'conv3d_backprop_input_v2_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrrfr`rarr rHrIrJrKrorLrMrNrOrPs rQconv3d_backprop_input_v2r_z sH    0h..0$ #\\  11 %t[&i)Wm[)-gn GdE] +  13: ; <<9@ @"X  r9 - @' @   gy 1'K!!+}=+I Ie} -  13< = >>=FFbx  [1F)F'88[.:G)0k+44 A!QX QK' ""$3%%c*Ill9%y#,,y2IS\\-8+ll;'  * ,F ::L vw@ (' .W  & &- ##At,,  # #    4 v|Wg!YTtMM  # #    AGs<#G6 I/ I46H= H$$H=<H=II,+I,zraw_ops.Conv3DBackpropInputV2c t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j |d}|gd}t|ttfstd|z|D cgc]} t j | d}} t j||g|tjtjtjtjg\} } | \}}t j|g|tjtjgtj\} \}|||g} d | d|d|d|d|d | f }t jd d | ||| }t j rt j"d| |||\}|Scc} wcc} w)Nr[rfr`rxrar-r\rr%r]sConv3DBackpropInputV2rsrTrZ)rkrlrmr5r;r<rnrVrWrXrYrZrurrrrArG)rrrrfr`rarr r!ror[r _attr_TshaperPrOrJs rQr^r^ s GdE] +  13: ; <<9@ @"X  r9 - @' @   gy 1'K!!+}=+I Ie} -  13< = >>=FFbx  [1F)F66 7MsU\UaUacjcscsu|vEvEGNGVGVUYZ'9$6 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. DepthwiseConv2dNativerfr`rrarNrfr`rrarr r!MExpected list for 'strides' argument to 'depthwise_conv2d_native' Op, not %r.WExpected list for 'explicit_paddings' argument to 'depthwise_conv2d_native' Op, not %r.r^rOExpected list for 'dilations' argument to 'depthwise_conv2d_native' Op, not %r.)rrrfr`rrarr r%)r&rr'r(rr)r,r-r.r/r0&depthwise_conv2d_native_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrfr`rrarr rHrIrJrKrorLrMrNrOrPs rQdepthwise_conv2d_nativer s`    0h..0$ #\\  11 %tUFIG%8:K{K 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. #DepthwiseConv2dNativeBackpropFilterrfr`rrarNr{]Expected list for 'strides' argument to 'depthwise_conv2d_native_backprop_filter' Op, not %r.gExpected list for 'explicit_paddings' argument to 'depthwise_conv2d_native_backprop_filter' Op, not %r.r^r_Expected list for 'dilations' argument to 'depthwise_conv2d_native_backprop_filter' Op, not %r.) rrrrfr`rrarr r%)r&rr'r(rr)r,r-r.r/r06depthwise_conv2d_native_backprop_filter_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrrfr`rrarr rHrIrJrKrorLrMrNrOrPs rQ'depthwise_conv2d_native_backprop_filterrS sP    0h..0$ #\\  11 3T5lIw 7. {Y  g n GdE] +  @BI J KK9@ @"X  r9 - @' @   gy 1' %e} 5  @BS T UUM^^bx((-@A^^K!!+}=+I Ie} -  @BK L MM=FFbx  [1F)F'88-U7>AR;F9BO!QX QK' ""$3%%c*Ill9%y#,,y2I!3<<0C#DS\\-8+ll;' )F ::L -|VWN (' .o  & &- ##At,,  # #    C |Wg-;Dd44  # #    A_GB%IJ>/KKJ I22J  J J$$J;:J;z+raw_ops.DepthwiseConv2dNativeBackpropFilterc t|ttfstd|z|D cgc]} t j | d}} t j |d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d}|gd}t|ttfstd |z|D cgc]} t j | d }} t j||g| tjtjtjtjg\} } | \}}tj|tj}|||g} d | d|d|d|d|d |f }t j d d | || |}t j"rt j$d| |||\}|Scc} wcc} wcc} w)Nrrfr`rrr^rarrrr%s#DepthwiseConv2dNativeBackpropFilterrsrTrr)rrrrfr`rrarr r!ror[rrPrOrJs rQrr sL GdE] +  @BI J KK9@ @"X  r9 - @' @   gy 1' %e} 5  @BS T UUM^^bx((-@A^^K!!+}=+I Ie} -  @BK L MM=FFbx  [1F)F66|7LcT[T`T`bibrbrt{uDuDFMFUFUTXY'9#5,'' gmmD,|4, )Wi(- y &   CQ$0C"& ('""$ -|VWN (' .C A_GG1 G61G;'TV_DepthwiseConv2dNativeBackpropInput_Tc tjxstj} | j} | jr' t j | d||||d|d|d|d|d|} | St|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|g}t|ttfst!d |z|D cgc]} t#j$| d}} |d }t#j&|d}|gd }t|ttfst!d |z|D cgc]} t#j$| d}} t)j*d||||||||| \}}}}|dd} t#j,rd|j/dd|j1dd|j1dd|j1dd|j1dd|j1df }|j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYTwxYwcc} wcc} wcc} w)aComputes the gradients of depthwise convolution with respect to the input. Args: input_sizes: A `Tensor` of type `int32`. An integer vector representing the shape of `input`, based on `data_format`. For example, if `data_format` is 'NHWC' then `input` is a 4-D `[batch, height, width, channels]` tensor. filter: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[filter_height, filter_width, in_channels, depthwise_multiplier]`. out_backprop: A `Tensor`. Must have the same type as `filter`. 4-D with shape based on `data_format`. For example, if `data_format` is 'NHWC' then out_backprop shape is `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. explicit_paddings: An optional list of `ints`. Defaults to `[]`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `filter`. "DepthwiseConv2dNativeBackpropInputrfr`rrarNr{\Expected list for 'strides' argument to 'depthwise_conv2d_native_backprop_input' Op, not %r.fExpected list for 'explicit_paddings' argument to 'depthwise_conv2d_native_backprop_input' Op, not %r.r^r^Expected list for 'dilations' argument to 'depthwise_conv2d_native_backprop_input' Op, not %r.) rrrrfr`rrarr r%)r&rr'r(rr)r,r-r.r/r05depthwise_conv2d_native_backprop_input_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrrfr`rrarr rHrIrJrKrorLrMrNrOrPs rQ&depthwise_conv2d_native_backprop_inputr sN    0h..0$ #\\  11 2D+vi)W. {Y  g n GdE] +  ?AH I JJ9@ @"X  r9 - @' @   gy 1' %e} 5  ?AR S TTM^^bx((-@A^^K!!+}=+I Ie} -  ?AJ K LL=FFbx  [1F)F'88,+5;;G6=6=@Q:E8AN!QX QK' ""$3%%c*Ill9%y#,,y2I!3<<0C#DS\\-8+ll;' )F ::L ,lFGM (' .o  & &- ##At,,  # #    B v|Wg-;Dd44  # #    A_Grz*raw_ops.DepthwiseConv2dNativeBackpropInputc t|ttfstd|z|D cgc]} t j | d}} t j |d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d}|gd}t|ttfstd |z|D cgc]} t j | d }} t j||g| tjtjtjtjg\} } | \}}tj|tj}|||g} d | d|d|d|d|d |f }t j d d | || |}t j"rt j$d| |||\}|Scc} wcc} wcc} w)Nrrfr`rrr^rarrrr%s"DepthwiseConv2dNativeBackpropInputrsrTrr)rrrrfr`rrarr r!ror[rrPrOrJs rQrrY sI GdE] +  ?AH I JJ9@ @"X  r9 - @' @   gy 1' %e} 5  ?AR S TTM^^bx((-@A^^K!!+}=+I Ie} -  ?AJ K LL=FFbx  [1F)F66 7MsU\UaUacjcscsu|vEvEGNGVGVUYZ'9$6<&&{GMMB+v|4, )Wi(- y &   BA$0C"& ('""$ ,lFGM (' .C A_GrTV_Dilation2D_Tc `tjxstj}|j}|jr" t j |d|||d|d|d| }|St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}t)j*d|||||| \} } } } | dd}t#j,rjd | j/d d| j1dd| j1dd| j1df}| j2}t#j4d||||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYwcc} wcc} w) a:Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors. The `input` tensor has shape `[batch, in_height, in_width, depth]` and the `filter` tensor has shape `[filter_height, filter_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default "NHWC" `data_format`. In detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with `conv2d`, we use unmirrored filters): output[b, y, x, c] = max_{dy, dx} input[b, strides[1] * y + rates[1] * dy, strides[2] * x + rates[2] * dx, c] + filter[dy, dx, c] Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros. Note on duality: The dilation of `input` by the `filter` is equal to the negation of the erosion of `-input` by the reflected `filter`. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 4-D with shape `[batch, in_height, in_width, depth]`. filter: A `Tensor`. Must have the same type as `input`. 3-D with shape `[filter_height, filter_width, depth]`. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: A list of `ints` that has length `>= 4`. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Dilation2Drfratesr`Nrfrr`r r!@Expected list for 'strides' argument to 'dilation2d' Op, not %r.>Expected list for 'rates' argument to 'dilation2d' Op, not %r.)rrrfrr`r r%)r&rr'r(rr)r,r-r.r/r0dilation2d_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrrfrr`r rHrIrJrKrorLrMrNrOrPs rQ dilation2dr s>Z    0h..0$ #\\ 11 lD%GW y'#gn GdE] +  #%, - ..9@ @"X  r9 - @' @ ED%= )  #%* + ,,5: :b8  R ) :% :   gy 1''88E&'!7?!QX QK' ""$3%%c*Ill9%w W0Ei02F::L lFG5 (' .E  & &- ##At,,  # #    & w  # #    A ;s< F/ H&H+/G6GG65G6:H H#"H#zraw_ops.Dilation2Dct|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}t j||g|tjtjtjtjtjtjtjtj tj"tj$tj&tj(g \}} | \}}||g} d|d|d|d|f} t j*dd| | || } t j,rt j.d | | | | \} | Scc}wcc}w) Nrrfrrr`r%s Dilation2DrsrTrrkrlrmr5r;r<rnrVrWrZrurrrrrrYrrXrrrrArG) rrrfrr`r r!ror[rrPrOrJs rQrr s% GdE] +  #%, - ..9@ @"X  r9 - @' @ ED%= )  #%* + ,,5: :b8  R ) :% :   gy 1'66vgoo_f_n_npwp}p}@G@M@MOVO\O\^e^j^jlslyly{B{K{KMTM[M[]d]i]ikrkyky{B{I{INLM'9/5&, )Wgui  &   ]Al#)s ?' ""$ lFG5 (' .% A ; G0GTV_Dilation2DBackpropFilter_Tcftjxstj}|j}|jr# t j |d||||d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}t)j*d||||||| \} } } }|dd} t#j,rjd | j/d d| j1dd| j1dd| j1df}| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w) aComputes the gradient of morphological 2-D dilation with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 4-D with shape `[batch, in_height, in_width, depth]`. filter: A `Tensor`. Must have the same type as `input`. 3-D with shape `[filter_height, filter_width, depth]`. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, depth]`. strides: A list of `ints` that has length `>= 4`. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: A list of `ints` that has length `>= 4`. 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Dilation2DBackpropFilterrfrr`NrPExpected list for 'strides' argument to 'dilation2d_backprop_filter' Op, not %r.NExpected list for 'rates' argument to 'dilation2d_backprop_filter' Op, not %r.rrrrfrr`r r%)r&rr'r(rr)r,r-r.r/r0)dilation2d_backprop_filter_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGrrrrfrr`r rHrIrJrKrorLrMrNrOrPs rQdilation2d_backprop_filterr sI.    0h..0$ #\\ 11 ($v|7GUIw@gn GdE] +  35< = >>9@ @"X  r9 - @' @ ED%= )  35: ; <<5: :b8  R ) :% :   gy 1''88"%1=,35,3$ @!QX QK' ""$3%%c*Ill9%w W0Ei02F::L "L&'C (' .I  & &- ##At,,  # #    6 we$00  # #    A ;<!F1 H)H.1G8GG87G8<HH&%H&z raw_ops.Dilation2DBackpropFilterct|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}t j|||g|tjtjtjtjtjtjtjtj tj"tj$tj&tj(g \} } | \}}}|||g} d| d|d|d|f} t j*dd| | || } t j,rt j.d | | | | \} | Scc}wcc}w) Nrrfrrr`r%sDilation2DBackpropFilterrsrTrrrrrrfrr`r r!ror[rrPrOrJs rQrrDsC GdE] +  35< = >>9@ @"X  r9 - @' @ ED%= )  35: ; <<5: :b8  R ) :% :   gy 1'66v|7TVY\c\k\kmtm|m|FLLNUN[N[]d]j]jlslxlxzAzGzGIPIYIY[b[i[ikrkwkwy@yGyGIPIWIW\Z['9"+5&,., )Wgui  &   8!$0C"& ('""$ "L&'C (' .' A ; G0GTV_Dilation2DBackpropInput_Tcftjxstj}|j}|jr# t j |d||||d|d|d| } | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d|z|D cgc]} t#j$| d}} t#j&|d}t)j*d||||||| \} } } }|dd} t#j,rjd | j/d d| j1dd| j1dd| j1df}| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYwcc} wcc} w) aComputes the gradient of morphological 2-D dilation with respect to the input. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 4-D with shape `[batch, in_height, in_width, depth]`. filter: A `Tensor`. Must have the same type as `input`. 3-D with shape `[filter_height, filter_width, depth]`. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, depth]`. strides: A list of `ints` that has length `>= 4`. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: A list of `ints` that has length `>= 4`. 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. Dilation2DBackpropInputrfrr`NrOExpected list for 'strides' argument to 'dilation2d_backprop_input' Op, not %r.MExpected list for 'rates' argument to 'dilation2d_backprop_input' Op, not %r.rr%)r&rr'r(rr)r,r-r.r/r0(dilation2d_backprop_input_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGrs rQdilation2d_backprop_inputrasH.    0h..0$ #\\ 11 'ufl7GUIw@gn GdE] +  24; < ==9@ @"X  r9 - @' @ ED%= )  249 : ;;5: :b8  R ) :% :   gy 1''88!v0tjxstj}|j}|jr t j |d||}|St||fd}|tur|S t/j0d||\}}}}|dd}t3j4r7d|j7df} |j8} t3j:d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||fd}|tur|St|||S#t j$rYtt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw#tt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw)aComputes the exponential linear function. The ELU function is defined as: * $ e ^ x - 1 $ if $ x < 0 $ * $ x $ if $ x >= 0 $ Examples: >>> tf.nn.elu(1.0) >>> tf.nn.elu(0.0) >>> tf.nn.elu(-1000.0) See [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) ](http://arxiv.org/abs/1511.07289) Args: features: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. EluNrr"rr r%)r&rr'r(rr)r,r-r.r/r0_dispatcher_for_elur2elu_eager_fallbackr4r5r6r7r elur8r9r:r?r@r;rArErFrG rr rHrIrJrKrLrMrNrOrPs rQrrs<    0h..0$ #\\ 11 eT8%g n," 44!Gn$ n  ):: t-Aq#x QK' ""$3%%c* +F::L  |VW. (' .W  & &- ##At,,  # #    # T T#g  &  4))  # #  z " "" T8$7 g  ..<< <  Z    r45 Gi,,::: n  PC&3G&D-9DD-,D-1E EG,AGGAHHz raw_ops.Eluc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sElursrTr r;rVrWrXrYrZrurrArGrr r!r[rPrOrJs rQrrs!88(S7<&   VQ|6!$4 1' ""$  |VW. (' .r\ TV_EluGrad_T gradientsoutputsctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aComputes gradients for the exponential linear (Elu) operation. Args: gradients: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. The backpropagated gradients to the corresponding Elu operation. outputs: A `Tensor`. Must have the same type as `gradients`. The outputs of the corresponding Elu operation. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. EluGradNrrrr r%)r&rr'r(rr)r,r-r.r/r0elu_grad_eager_fallbackr4r?r@r;rArErFrG rrr rHrIrJrKrLrMrNrOrPs rQelu_gradr*s>    0h..0$ #\\ 11 iy'3g n(88YdD!QX QK' ""$3%%c* +F::L <2 (' .'  & &- ##At,,  # #    $ W4T33  # #   rzraw_ops.EluGradcftj||g|tjtjtj tj g\}}|\}}||g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sEluGradrsrTrr rrr r!r[rrPrOrJs rQrrVs66 77KSSZS_S_ahaqaqsztCtCELETETSWX'9"9gW%, >&   Zd||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||| S#tj$rY.wxYwcc} w)a Performs fractional average pooling on the input. Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region. Args: value: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length `>= 4`. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. deterministic: An optional `bool`. Defaults to `False`. When set to True, a fixed pooling region will be used when iterating over a FractionalAvgPool node in the computation graph. Mainly used in unit test to make FractionalAvgPool deterministic. seed: An optional `int`. Defaults to `0`. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. An second seed to avoid seed collision. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, row_pooling_sequence, col_pooling_sequence). output: A `Tensor`. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. r pooling_ratiorrrrrNrrrrrrr r!OExpected list for 'pooling_ratio' argument to 'fractional_avg_pool' Op, not %r.Frr_rrrrrrr r%) r&rr'r(rr)_FractionalAvgPoolOutputr+r,r-r.r/r0"fractional_avg_pool_eager_fallbackr4rkrlrmr5r;r=r>r<r?r@rArCrDrBrErFrGr_rrrrrrr rHrIrJrK_frLrMrNrOrPs rQfractional_avg_poolrjsh    0h..0$ #\\  11 !4 }_mVT7 g )..w7g n MD%= 1  ,.; < ==GTT8&&r?;T-TM$$]OD-K""; >+M$$]OD- \ D   4 ($ ] E   E7 +%'885 +8)4+8t#(t 5!QX QK' ""$s||Or<rVrWrZrurrrrArGrr+r_rrrrrrr r!rr[rPrOrJs rQrr MD%= 1  ,.; < ==GTT8&&r?;T-TM$$]OD-K""; >+M$$]OD- \ D   4 ($ ] E   E7 +%55ugsW__V]VeVegngtgtv}wDwDEGH'8E, ]O]o}fd 5#w &   11\#)s ?' ""$ \67< $ * *7 3' .7UFTV_FractionalAvgPoolGrad_Torig_input_tensor_shaperrc tjxstj}|j}|jr t j |d|||||d| }|S|d}tj|d}tj d||||||\} } } } | dd}tj"rHd| j%dd| j'df} | j(}tj*d|| ||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYw)aComputes gradient of the FractionalAvgPool function. Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor. Args: orig_input_tensor_shape: A `Tensor` of type `int64`. Original input tensor shape for `fractional_avg_pool` out_backprop: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_avg_pool`. row_pooling_sequence: A `Tensor` of type `int64`. row pooling sequence, form pooling region with col_pooling_sequence. col_pooling_sequence: A `Tensor` of type `int64`. column pooling sequence, form pooling region with row_pooling sequence. overlapping: An optional `bool`. Defaults to `False`. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `out_backprop`. FractionalAvgPoolGradrNrr r!F)rrrrrr r%)r&rr'r(rr)r,r-r.r/r0'fractional_avg_pool_grad_eager_fallbackr4r;r>r?r@rArDrErFrG)rrrrrr rHrIrJrKrLrMrNrOrPs rQfractional_avg_pool_gradrsH    0h..0$ #\\  11 %t-D*,@{$gnK""; >+'889P.:6J6J-8t E!QX QK' ""$S// >  %'F::L vw@ (' .9  & &- ##At,,  # #    4 !<1E KdNN  # #   s0DED//EE EE54E5zraw_ops.FractionalAvgPoolGradcr|d}tj|d}tj|g|tjtj tj tjg\}\}tj|tj}tj|tj}tj|tj}||||g}d|d|f} tjdd|| ||} tjrtjd|| | | \} | S)NFrr%sFractionalAvgPoolGradrsrTr r;r>rVrWrZrurrr.rrrArG) rrrrrr r!r[rPrOrJs rQrrSs0K""; >+%<d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||| S#tj$rY.wxYwcc} w)avPerforms fractional max pooling on the input. Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following: 1. input_row_length : the number of rows from the input set 2. output_row_length : which will be smaller than the input 3. alpha = input_row_length / output_row_length : our reduction ratio 4. K = floor(alpha) 5. row_pooling_sequence : this is the result list of pool boundary rows Then, row_pooling_sequence should satisfy: 1. a[0] = 0 : the first value of the sequence is 0 2. a[end] = input_row_length : the last value of the sequence is the size 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size 4. length(row_pooling_sequence) = output_row_length+1 For more details on fractional max pooling, see this paper: [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) Args: value: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length `>= 4`. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to True, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. deterministic: An optional `bool`. Defaults to `False`. When set to True, a fixed pooling region will be used when iterating over a FractionalMaxPool node in the computation graph. Mainly used in unit test to make FractionalMaxPool deterministic. seed: An optional `int`. Defaults to `0`. If either seed or seed2 are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed. seed2: An optional `int`. Defaults to `0`. An second seed to avoid seed collision. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, row_pooling_sequence, col_pooling_sequence). output: A `Tensor`. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. rrrrrrrNrOExpected list for 'pooling_ratio' argument to 'fractional_max_pool' Op, not %r.Frrr%) r&rr'r(rr)_FractionalMaxPoolOutputr+r,r-r.r/r0"fractional_max_pool_eager_fallbackr4rkrlrmr5r;r=r>r<r?r@rArCrDrBrErFrGrs rQfractional_max_poolrlsX    0h..0$ #\\  11 !4 }_mVT7 g )..w7g n MD%= 1  ,.; < ==GTT8&&r?;T-TM$$]OD-K""; >+M$$]OD- \ D   4 ($ ] E   E7 +%'885 +8)4+8t#(t 5!QX QK' ""$s||Or<rVrWrZrurrrrArGrr+rs rQrrrrTV_FractionalMaxPoolGrad_T orig_input orig_outputc tjxstj}|j}|jr! t j |d||||||d| } | S|d}tj|d}tj d|||||||\} } } } | dd} tj"rHd| j%dd| j'df}| j(}tj*d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYwxYw)a"Computes gradient of the FractionalMaxPool function. Args: orig_input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `int64`. Original input for `fractional_max_pool` orig_output: A `Tensor`. Must have the same type as `orig_input`. Original output for `fractional_max_pool` out_backprop: A `Tensor`. Must have the same type as `orig_input`. 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_max_pool`. row_pooling_sequence: A `Tensor` of type `int64`. row pooling sequence, form pooling region with col_pooling_sequence. col_pooling_sequence: A `Tensor` of type `int64`. column pooling sequence, form pooling region with row_pooling sequence. overlapping: An optional `bool`. Defaults to `False`. When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `orig_input`. FractionalMaxPoolGradrNrF)rrrrrrr r%)r&rr'r(rr)r,r-r.r/r0'fractional_max_pool_grad_eager_fallbackr4r;r>r?r@rArDrErFrG)rrrrrrr rHrIrJrKrLrMrNrOrPs rQfractional_max_pool_gradrs@    0h..0$ #\\  11 %tZ*,@{$gnK""; >+'88J-8.:6J6J-8t E!QX QK' ""$S// >  %'F::L vw@ (' .;  & &- ##At,,  # #    4 k<1E KdNN  # #   s0DE D11E  E E!!E87E8zraw_ops.FractionalMaxPoolGradc8|d}tj|d}tj|||g|tjtj tj tjg\}} | \}}}tj|tj}tj|tj}|||||g} d|d|f} tjdd| | ||} tjrtjd| | | | \} | S)NFrr%sFractionalMaxPoolGradrsrTrr) rrrrrrr r!r[rrPrOrJs rQrrjs7K""; >+66 KQ]7^`cfmfufuw~xGxGIPIVIVX_XeXefhi'9,5):{L//0DgmmT//0DgmmTk<9MOcd, ;W 5&   5q#)s ?' ""$ vw@ (' .r\FusedBatchNorm)y batch_meanbatch_variancereserve_space_1reserve_space_2TV_FusedBatchNorm_T)boundscaleoffsetmeanvarianceepsilonexponential_avg_factor is_trainingc \tjxstj} | j} | jr< t j | d| |||||d|d|d|d|} t j| } | S|d}tj |d}|d }tj |d}|d }tj"|d}|d }tj$|d}t'j(d|||||||||| \}}}}|dd} tj*r{d |j-d d|j/dd|j/dd|j/dd|j1df }|j2}tj4d||| t j| } | S#tj$r } tj| | Yd} ~ nd} ~ wtj$rYnwxYw t|||||||||| |  S#tj$rYwxYw)aBatch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. Args: x: A `Tensor`. Must be one of the following types: `float32`. A 4D Tensor for input data. scale: A `Tensor`. Must have the same type as `x`. A 1D Tensor for scaling factor, to scale the normalized x. offset: A `Tensor`. Must have the same type as `x`. A 1D Tensor for offset, to shift to the normalized x. mean: A `Tensor`. Must have the same type as `x`. A 1D Tensor for population mean. Used for inference only; must be empty for training. variance: A `Tensor`. Must have the same type as `x`. A 1D Tensor for population variance. Used for inference only; must be empty for training. epsilon: An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x. exponential_avg_factor: An optional `float`. Defaults to `1`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. The data format for x and y. Either "NHWC" (default) or "NCHW". is_training: An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2). y: A `Tensor`. Has the same type as `x`. batch_mean: A `Tensor`. Has the same type as `x`. batch_variance: A `Tensor`. Has the same type as `x`. reserve_space_1: A `Tensor`. Has the same type as `x`. reserve_space_2: A `Tensor`. Has the same type as `x`. rrrrarNrrrarr r!-C6?rsr^T rcr r r r rrrarr r%)r&rr'r(rr)_FusedBatchNormOutputr+r,r-r.r/r0 _fused_batch_norm_eager_fallbackr4r;r=rnr>r?r@rArErCrDrFrGrcr r r r rrrarr rHrIrJrKrLrMrNrOrPs rQ_fused_batch_normrsTL    0h..0$ #\\  11 ah746L{M;@g&++G4g n _G    3'##../EG_`K!!+}=+K""; >+'88AU6#+W1G&1{# %!QX QK' ""$3%%c*Ill9%'?ll34mll=)=  / 1F ::L ,9 ! ' ' 0' .U  & &- ##At,,  # #    - UFD(G!7!{   # #   s0:F33G:G!!G:9G:>HH+*H+zraw_ops.FusedBatchNormc  |d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|||||g| t j g\} } | \}}}}}|||||g} d | d|d|d|d|f }tjd d | || | }tjrtjd | ||tj|}|S)Nrrrsrr^raTrr%sFusedBatchNormrrTr) r;r=rnr>rVrWrZrrArGrr+)rcr r r r rrrarr r!r[rrPrOrJs rQrrs@ _G    3'##../EG_`K!!+}=+K""; >+665&$PX7Y[^ahapap`st'9'0$1eVT8UFD(3, )W.F-m  &   .,#)s ?' ""$ ,9 ! ' ' 0' .r\FusedBatchNormGrad) x_backpropscale_backpropoffset_backpropreserve_space_3reserve_space_4TV_FusedBatchNormGrad_T y_backproprrc tjxstj} | j} | jr: t j | d||||||d|d|d|} t j| } | S|d}tj |d}|d}tj"|d}|d }tj$|d}t'j(d||||||||| \} } }}|dd} tj*rjd |j-d d|j/dd|j/dd|j1df}|j2}tj4d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||||  S#tj$rYwxYw) aGradient for batch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. Args: y_backprop: A `Tensor`. Must be one of the following types: `float32`. A 4D Tensor for the gradient with respect to y. x: A `Tensor`. Must have the same type as `y_backprop`. A 4D Tensor for input data. scale: A `Tensor`. Must have the same type as `y_backprop`. A 1D Tensor for scaling factor, to scale the normalized x. reserve_space_1: A `Tensor`. Must have the same type as `y_backprop`. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation. reserve_space_2: A `Tensor`. Must have the same type as `y_backprop`. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation. epsilon: An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW". is_training: An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4). x_backprop: A `Tensor`. Has the same type as `y_backprop`. scale_backprop: A `Tensor`. Has the same type as `y_backprop`. offset_backprop: A `Tensor`. Has the same type as `y_backprop`. reserve_space_3: A `Tensor`. Has the same type as `y_backprop`. reserve_space_4: A `Tensor`. Has the same type as `y_backprop`. rrrarNrrarr r!rr^T r!rcr rrrrarr r%)r&rr'r(rr)_FusedBatchNormGradOutputr+r,r-r.r/r0$fused_batch_norm_grad_eager_fallbackr4r;r=rnr>r?r@rArErCrDrFrGr!rcr rrrrarr rHrIrJrKrLrMrNrOrPs rQfused_batch_norm_gradr(sV    0h..0$ #\\  11 "D*a)Wm]K1g*//8g n _G    3'K!!+}=+K""; >+'88q.=.=&-;*5D B!QX QK' ""$3%%c*Ill9%}ll=)=  /1F::L lFG= % + +G 4' .K  & &- ##At,,  # #    1 a{   # #   s08FG F33G  G G%%G<;G<zraw_ops.FusedBatchNormGradc |d}tj|d}|d}tj|d}|d}tj|d}tj|||||g| t j g\} } | \}}}}}|||||g} d| d|d|d|f} tjdd | | | | }tjrtjd | | |tj|}|S) Nrrr^raTrr%sFusedBatchNormGradrrTr) r;r=rnr>rVrWrZrrArGr%r+)r!rcr rrrrarr r!r[rrPrOrJs rQr&r&cs& _G    3'K!!+}=+K""; >+66 Auo_n7oqtw~xGxGwJK'9=F::q%/aI, )Wm[ &   2Al#)s ?' ""$ lFG= % + +G 4' .r\FusedBatchNormGradV2TV_FusedBatchNormGradV2_TTV_FusedBatchNormGradV2_Uc  tjxstj} | j} | jr: t j | d||||||d|d|d|} t j| } | S|d}tj |d}|d}tj"|d}|d }tj$|d}t'j(d||||||||| \} } }}|dd} tj*r{d |j-d d |j-d d|j/dd|j/dd|j1df }|j2}tj4d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||||  S#tj$rYwxYw) aGradient for batch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. Args: y_backprop: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. A 4D Tensor for the gradient with respect to y. x: A `Tensor`. Must have the same type as `y_backprop`. A 4D Tensor for input data. scale: A `Tensor` of type `float32`. A 1D Tensor for scaling factor, to scale the normalized x. reserve_space_1: A `Tensor`. Must be one of the following types: `float32`. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation. reserve_space_2: A `Tensor`. Must have the same type as `reserve_space_1`. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation. epsilon: An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW". is_training: An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (x_backprop, scale_backprop, offset_backprop, reserve_space_3, reserve_space_4). x_backprop: A `Tensor`. Has the same type as `y_backprop`. scale_backprop: A `Tensor`. Has the same type as `reserve_space_1`. offset_backprop: A `Tensor`. Has the same type as `reserve_space_1`. reserve_space_3: A `Tensor`. Has the same type as `reserve_space_1`. reserve_space_4: A `Tensor`. Has the same type as `reserve_space_1`. r*rrarNr#rr^Tr$r%U)r&rr'r(rr)_FusedBatchNormGradV2Outputr+r,r-r.r/r0'fused_batch_norm_grad_v2_eager_fallbackr4r;r=rnr>r?r@rArErCrDrFrGr's rQfused_batch_norm_grad_v2r1s'V    0h..0$ #\\  11 $dJ5)Wm]K1g,11':g n _G    3'K!!+}=+K""; >+'88:%0?0?(/[,7d D!QX QK' ""$3%%c*C1C1CC1Hi0-ll=)=  /1F::L  fg? ' - -g 6' .K  & &- ##At,,  # #    4 a{   # #   s08FG)GGG!G66H  H zraw_ops.FusedBatchNormGradV2c |d}tj|d}|d}tj|d}|d}tj|d}tj||g| t j t jt jg\} } | \}}tj||g| t jg\} } | \}}tj|t j}|||||g}d| d| d|d|d|f }tjd d ||| | }tjrtjd |||tj|}|S) Nrrr^raTrr%r.sFusedBatchNormGradV2rrTr*)r;r=rnr>rVrWrXrYrZr.rrrArGr/r+)r!rcr rrrrarr r!r[r_attr_U _inputs_UrPrOrJs rQr0r0sj _G    3'K!!+}=+K""; >+66 Agll\c\l\lnun}n}NAB'9/:q667Y[^ahapap`st'9'0$?O   8%aI, #w 7M }k +&   4a #)s ?' ""$  fg? ' - -g 6' .r\FusedBatchNormGradV3)rrrrreserve_space_5TV_FusedBatchNormGradV3_TTV_FusedBatchNormGradV3_Urc &tjxstj} | j} | jr; t j | d| ||||||d|d|d|} t j| } | S|d}tj |d}|d}tj"|d}|d }tj$|d}t'j(d|||||||||| \}}}}|dd} tj*r{d |j-d d |j-d d|j/dd|j/dd|j1df }|j2}tj4d||| t j| } | S#tj$r } tj| | Yd} ~ nd} ~ wtj$rYnwxYw t|||||||||| |  S#tj$rYwxYw) a Gradient for batch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. Args: y_backprop: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. A 4D Tensor for the gradient with respect to y. x: A `Tensor`. Must have the same type as `y_backprop`. A 4D Tensor for input data. scale: A `Tensor` of type `float32`. A 1D Tensor for scaling factor, to scale the normalized x. reserve_space_1: A `Tensor`. Must be one of the following types: `float32`. When is_training is True, a 1D Tensor for the computed batch mean to be reused in gradient computation. When is_training is False, a 1D Tensor for the population mean to be reused in both 1st and 2nd order gradient computation. reserve_space_2: A `Tensor`. Must have the same type as `reserve_space_1`. When is_training is True, a 1D Tensor for the computed batch variance (inverted variance in the cuDNN case) to be reused in gradient computation. When is_training is False, a 1D Tensor for the population variance to be reused in both 1st and 2nd order gradient computation. reserve_space_3: A `Tensor`. Must have the same type as `reserve_space_1`. When is_training is True, a 1D Tensor for some intermediate results to be reused in gradient computation. When is_training is False, a dummy empty Tensor will be created. epsilon: An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x. data_format: An optional `string` from: `"NHWC", "NCHW", "NDHWC", "NCDHW"`. Defaults to `"NHWC"`. The data format for y_backprop, x, x_backprop. Either "NHWC" (default) or "NCHW". is_training: An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (x_backprop, scale_backprop, offset_backprop, reserve_space_4, reserve_space_5). x_backprop: A `Tensor`. Has the same type as `y_backprop`. scale_backprop: A `Tensor`. Has the same type as `reserve_space_1`. offset_backprop: A `Tensor`. Has the same type as `reserve_space_1`. reserve_space_4: A `Tensor`. Has the same type as `reserve_space_1`. reserve_space_5: A `Tensor`. Has the same type as `reserve_space_1`. r5rrarNr#rr^T) r!rcr rrrrrarr r%r.)r&rr'r(rr)_FusedBatchNormGradV3Outputr+r,r-r.r/r0'fused_batch_norm_grad_v3_eager_fallbackr4r;r=rnr>r?r@rArErCrDrFrG)r!rcr rrrrrarr rHrIrJrKrLrMrNrOrPs rQfused_batch_norm_grad_v3r<s/^    0h..0$ #\\  11 $dJ5/9g{M;@g,11':g n _G    3'K!!+}=+K""; >+'88:%0?0?0?(/[,7d D!QX QK' ""$3%%c*C1C1CC1Hi0-ll=)=  /1F::L  fg? ' - -g 6' .M  & &- ##At,,  # #    4 a 7 !$88  # #   s09FG+GGG#G99HHzraw_ops.FusedBatchNormGradV3c |d}tj|d}|d}tj|d}|d}tj|d}tj||g| t j t jt jg\} } | \}}tj|||g| t jg\} }|\}}}tj|t j}||||||g}d| d| d|d|d|f }tjd d ||| | }tjrtjd |||tj|}|S) Nrrr^raTrr%r.sFusedBatchNormGradV3rrTr5)r;r=rnr>rVrWrXrYrZr.rrrArGr:r+)r!rcr rrrrrarr r!r[rr3r4rPrOrJs rQr;r;gs _G    3'K!!+}=+K""; >+66 Agll\c\l\lnun}n}NAB'9/:q66Zi7jlorysBsBrEF'98A5?O_   8%a/Z, #w 7M }k +&   4a #)s ?' ""$  fg? ' - -g 6' .r\FusedBatchNormV2TV_FusedBatchNormV2_TTV_FusedBatchNormV2_Uc ~tjxstj} | j} | jr< t j | d| |||||d|d|d|d|} t j| } | S|d}tj |d}|d }tj |d}|d }tj"|d}|d }tj$|d}t'j(d|||||||||| \}}}}|dd} tj*rd |j-d d|j-dd|j/dd|j/dd|j/dd|j1df }|j2}tj4d||| t j| } | S#tj$r } tj| | Yd} ~ nd} ~ wtj$rYnwxYw t|||||||||| |  S#tj$rYwxYw)aBatch normalization. Note that the size of 4D Tensors are defined by either "NHWC" or "NCHW". The size of 1D Tensors matches the dimension C of the 4D Tensors. Args: x: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. A 4D Tensor for input data. scale: A `Tensor`. Must be one of the following types: `float32`. A 1D Tensor for scaling factor, to scale the normalized x. offset: A `Tensor`. Must have the same type as `scale`. A 1D Tensor for offset, to shift to the normalized x. mean: A `Tensor`. Must have the same type as `scale`. A 1D Tensor for population mean. Used for inference only; must be empty for training. variance: A `Tensor`. Must have the same type as `scale`. A 1D Tensor for population variance. Used for inference only; must be empty for training. epsilon: An optional `float`. Defaults to `0.0001`. A small float number added to the variance of x. exponential_avg_factor: An optional `float`. Defaults to `1`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. The data format for x and y. Either "NHWC" (default) or "NCHW". is_training: An optional `bool`. Defaults to `True`. A bool value to indicate the operation is for training (default) or inference. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (y, batch_mean, batch_variance, reserve_space_1, reserve_space_2). y: A `Tensor`. Has the same type as `x`. batch_mean: A `Tensor`. Has the same type as `scale`. batch_variance: A `Tensor`. Has the same type as `scale`. reserve_space_1: A `Tensor`. Has the same type as `scale`. reserve_space_2: A `Tensor`. Has the same type as `scale`. r>rrrarNrrrsr^Trr%r.)r&rr'r(rr)_FusedBatchNormV2Outputr+r,r-r.r/r0"fused_batch_norm_v2_eager_fallbackr4r;r=rnr>r?r@rArErCrDrFrGrs rQfused_batch_norm_v2rDscL    0h..0$ #\\  11  $5&$746L{M;@g(--g6g n _G    3'##../EG_`K!!+}=+K""; >+'88auV$%-w3I(3!% '!QX QK' ""$3%%c*C1C1CC1Hi02Jll34mll=)=  / 1F ::L L&'; # ) )' 2' .U  & &- ##At,,  # #    / UFD(G!7!{   # #   0:GH G22H  H H%%H<;H<zraw_ops.FusedBatchNormV2c |d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|g| t j t jt jg\} \}tj||||g| t jg\} } | \}}}}|||||g}d | d | d|d|d|d|f }tjd d ||| | }tjrtjd|||tj|}|S)Nrrrsrr^raTrr%r.sFusedBatchNormV2rrTr>)r;r=rnr>rVrWrXrYrZrrArGrBr+rcr r r r rrrarr r!r[r3r4rPrOrJs rQrCrCss _G    3'##../EG_`K!!+}=+K""; >+111#sW\\7K[K[]d]l]lr?r@rArErCrDrFrGrs rQfused_batch_norm_v3rN scN    0h..0$ #\\  11  $5&$746L{M;@g(--g6g n _G    3'##../EG_`K!!+}=+K""; >+'88auV$%-w3I(3!% '!QX QK' ""$3%%c*C1C1CC1Hi02Jll34mll=)=  / 1F ::L L&'; # ) )' 2' .U  & &- ##At,,  # #    / UFD(G!7!{   # #   rEzraw_ops.FusedBatchNormV3c |d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|g| t j t jt jg\} \}tj||||g| t jt jg\} } | \}}}}|||||g}d | d | d|d|d|d|f }tjd d ||| | }tjrtjd|||tj|}|S)Nrrrsrr^raTrr%r.sFusedBatchNormV3rTrH)r;r=rnr>rVrWrXrYrZrrArGrLr+rGs rQrMrMls _G    3'##../EG_`K!!+}=+K""; >+111#sW\\7K[K[]d]l]lr?r@rArErDrCrFrG)rr[rRrrSrfr`r\r rHrIrJrKrorLrMrNrOrPs rQfused_resize_and_pad_conv2drasRN    0h..0$ #\\  11 'udHf 4fdIG%gn   4 ($ GdE] +  46= > ??9@ @"X  r9 - @' @   gy 1'! !++,@BXY'88!TH*0tW+28L(, .!QX QK' ""$3%%c*,B  !78&ll6"Is||I/Fi02F::L !<B (' .M  & &- ##At,,  # #    7 x3$74AA  # #    As6$F0#H*0G7GG76G7;HH'&H'zraw_ops.FusedResizeAndPadConv2Dc tj|d}t|ttfst d|z|D cgc]} tj | d}} tj|d}|d}tj|d}tj||g| tjtjtjg\} } | \}}tj|tj}tj|tj}||||g} d| d|d|d|d|f }tj dd | || | }tj"rtj$d | |||\}|Scc} w) NrSr_rfr`Fr\r%sFusedResizeAndPadConv2DrsrTr^)r;rnrkrlrmr5r<r>rVrWrXrZrur.rrrrArG)rr[rRrrSrfr`r\r r!ror[rrPrOrJs rQr`r`Ms   4 ($ GdE] +  46= > ??9@ @"X  r9 - @' @   gy 1'! !++,@BXY66vgll\c\k\kmtm|m|MA'9/5&  gmm 4$  # #Hgmm <(x0, 02F$ 7Iw 8&   7$0C"& ('""$ !<B (' .' AsF TV_InTopK_T predictionstargetsc tjxstj}|j}|jr t j |d|||d|}|Stj|d}tj d||||\}}} } | dd}tj"rHd| j%dd| j'df} | j(} tj*d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rY wxYw)a|Says whether the targets are in the top `K` predictions. This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the prediction for the target class is among the top `k` predictions among all predictions for example `i`. Note that the behavior of `InTopK` differs from the `TopK` op in its handling of ties; if multiple classes have the same prediction value and straddle the top-`k` boundary, all of those classes are considered to be in the top `k`. More formally, let \\(predictions_i\\) be the predictions for all classes for example `i`, \\(targets_i\\) be the target class for example `i`, \\(out_i\\) be the output for example `i`, $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ Args: predictions: A `Tensor` of type `float32`. A `batch_size` x `classes` tensor. targets: A `Tensor`. Must be one of the following types: `int32`, `int64`. A `batch_size` vector of class ids. k: An `int`. Number of top elements to look at for computing precision. name: A name for the operation (optional). Returns: A `Tensor` of type `bool`. InTopKrN)rr r!rdrerr r%)r&rr'r(rr)r,r-r.r/r0in_top_k_eager_fallbackr4r;r<r?r@rArBrErFrG rdrerr rHrIrJrKrLrMrNrOrPs rQin_top_krkksg:    0h..0$ #\\ 11 hk7C&   [!L#)s ?' ""$ L&'3 (' .r\IsotonicRegressionrsegmentsTV_IsotonicRegression_T"TV_IsotonicRegression_output_dtype output_dtypecFtjxstj}|j}|jr2 t j |d||d|}t j|}|S|tj }t#j$|d}t'j(d|||\}}}} | dd}t#j*rHd|j-dd|j-df} |j.} t#j0d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||S#tj$rY.wxYw)aSolves a batch of isotonic regression problems. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. A (batch_size, dim)-tensor holding a batch of inputs. output_dtype: An optional `tf.DType` from: `tf.half, tf.bfloat16, tf.float32, tf.float64`. Defaults to `tf.float32`. Dtype of output. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, segments). output: A `Tensor` of type `output_dtype`. segments: A `Tensor` of type `int32`. rsrwN)rwr r!)rrwr r%)r&rr'r(rr)_IsotonicRegressionOutputr+r,r-r.r/r0"isotonic_regression_eager_fallbackr4rWrZr; make_typer?r@rArErFrG) rrwr rHrIrJrKrLrMrNrOrPs rQisotonic_regressionr| s    0h..0$ #\\ 11 "D%Og)//8g n??L##L.A,'88E #')!QX QK' ""$3%%c*N  02F::L lFG= % + +G 4' .1  & &- ##At,,  # #    / l4AA  # #   s00D//E6EE65E6:F F F zraw_ops.IsotonicRegressionc|tj}tj|d}tj|g|tjtj tj tjtjtjtjtjtjtjtjtjg \}\}|g}d|d|f}tj dd||||}tj"rtj$d|||t&j)|}|S)Nrwr%sIsotonicRegressionrSrTrs)rWrZr;r{rVrurrrrrrYrrXrrrrArGryr+)rrwr r!r[rPrOrJs rQrzrzAs??L##L.A,55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjcpcpryrBrBDKDRDRT[T`T`bibpbpryr@r@ECD'8E, ., 7&   2Al#)s ?' ""$ lFG= % + +G 4' .r\ TV_L2Loss_Tz nn.l2_lossc >tjxstj}|j}|jr t j |d||}|St||fd}|tur|S t/j0d||\}}}}|dd}t3j4r7d|j7df} |j8} t3j:d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||fd}|tur|St|||S#t j$rYtt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw#tt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw)aiL2 Loss. Computes half the L2 norm of a tensor without the `sqrt`: output = sum(t ** 2) / 2 Args: t: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. Typically 2-D, but may have any dimensions. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `t`. L2LossNrr")rr r%)r&rr'r(rr)r,r-r.r/r0_dispatcher_for_l2_lossr2l2_loss_eager_fallbackr4r5r6r7r l2_lossr8r9r:r?r@r;rArErFrG) rr rHrIrJrKrLrMrNrOrPs rQrrSs$    0h..0$ #\\ 11 ha!g n,& D DGn$ n  )::AD"Aq#x QK' ""$3%%c* +F::L ,1 (' .W  & &- ##At,,  # #    ' d*dg  & # $D""  # #  z " "" R- g  ..<< <  Z    2tad+ Gi,,::: n  rzraw_ops.L2Lossc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sL2LossrsrTrr)rr r!r[rPrOrJs rQrrs111#sW\\7K[K[]d]l]lnun}n}=AB-'4A, >&   Y,f!$4 1' ""$ ,1 (' .r\TV_LRN_Tznn.local_response_normalizationznn.lrn depth_radiusalphactjxstj}|j}|jr# t j |d||d|d|d|d| }|St||||||fd}|tur|S|d }t/j0|d}|d }t/j2|d}|d }t/j2|d}|d }t/j2|d} t5j6d|||||| \} } } } | dd}t/j8r{d| j;dd| j=dd| j=dd| j=dd | j?d f } | j@}t/jBd|| ||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||fd}|tur|St|||||||S#t j$rYtt f$rLt#j$t&dt)|||||| }|t"j*j,ur|cYSwxYw#tt f$rLt#j$t&dt)|||||| }|t"j*j,ur|cYSwxYw)aLocal Response Normalization. The 4-D `input` tensor is treated as a 3-D array of 1-D vectors (along the last dimension), and each vector is normalized independently. Within a given vector, each component is divided by the weighted, squared sum of inputs within `depth_radius`. In detail, sqr_sum[a, b, c, d] = sum(input[a, b, c, d - depth_radius : d + depth_radius + 1] ** 2) output = input / (bias + alpha * sqr_sum) ** beta For details, see [Krizhevsky et al., ImageNet classification with deep convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks). Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. 4-D. depth_radius: An optional `int`. Defaults to `5`. 0-D. Half-width of the 1-D normalization window. bias: An optional `float`. Defaults to `1`. An offset (usually positive to avoid dividing by 0). alpha: An optional `float`. Defaults to `1`. A scale factor, usually positive. beta: An optional `float`. Defaults to `0.5`. An exponent. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. LRNrrrrNrrrrr r!r")rrrrrr rrs?r%)"r&rr'r(rr)r,r-r.r/r0_dispatcher_for_lrnr2lrn_eager_fallbackr4r5r6r7r lrnr8r9r:r;r<r=r?r@rArBrCrErFrG)rrrrrr rHrIrJrKrLrMrNrOrPs rQrrsB    0h..0$ #\\ 11 eT5.,&gn0"  dE47?Gn$ nL""<@, \ D   T6 *$ ] E   eW -% \ D   T6 *$  ):: UDt%Aq#x QK' ""$c//?ll6"GS\\'-BFll6"C););C)@BF::L  |VW. (' .{  & &- ##At,,  # #    # ,eT4 94Ag  &  lU  # #  z " "" TLt %Dt= g  ..<< <  < Z    r4e,T#$T; Gi,,::: n  sP!F"'!J"G)5GG)(G)-H HJ 1AJ  J AK)'K)z raw_ops.LRNc <|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|g|tj tj tjgtj\}\}|g}d|d|d|d|d|f } tjd d|| || } tjrtjd || | | \} | S) Nrrrsrrrrr%sLRNrTr r;r<r=rVrWrXrYrZrrArG) rrrrrr r!r[rPrOrJs rQrrs1L""<@, \ D   T6 *$ ] E   eW -% \ D   T6 *$55ugsW\\SZScSceletetDwzAzIzIJ'8E, L&$$W &   VQ|6!$4 1' ""$  |VW. (' .r\ TV_LRNGrad_T input_grads input_image output_imagectjxstj}|j} | jr% t j |d||||d|d|d|d|} | S|d}tj|d}|d }tj|d}|d }tj|d}|d }tj|d}t!j"d|||||||| \} } } }|dd} tj$r{d| j'dd| j)dd| j)dd| j)dd | j+d f }| j,}tj.d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYw) aGradients for Local Response Normalization. Args: input_grads: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. 4-D with shape `[batch, height, width, channels]`. input_image: A `Tensor`. Must have the same type as `input_grads`. 4-D with shape `[batch, height, width, channels]`. output_image: A `Tensor`. Must have the same type as `input_grads`. 4-D with shape `[batch, height, width, channels]`. depth_radius: An optional `int`. Defaults to `5`. A depth radius. bias: An optional `float`. Defaults to `1`. An offset (usually > 0 to avoid dividing by 0). alpha: An optional `float`. Defaults to `1`. A scale factor, usually positive. beta: An optional `float`. Defaults to `0.5`. An exponent. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input_grads`. LRNGradrrrrNrrrsr)rrrrrrrr r%)r&rr'r(rr)r,r-r.r/r0lrn_grad_eager_fallbackr4r;r<r=r?r@rArBrCrErFrG)rrrrrrrr rHrIrJrKrLrMrNrOrPs rQlrn_gradr4s *    0h..0$ #\\  11 i{K fdGUF gnL""<@, \ D   T6 *$ ] E   eW -% \ D   T6 *$'88{ ,<E4A!QX QK' ""$c//?ll6"GS\\'-BFll6"C););C)@BF::L <2 (' .I  & &- ##At,,  # #    $ {L|5t$DBB  # #   s0#F GF77GGG((G?>G?zraw_ops.LRNGradc L|d}tj|d}|d}tj|d}|d}tj|d}|d}tj|d}tj|||g|tj tj tjgtj\} } | \}}}|||g} d|d|d|d|d| f } tjd d| | || } tjrtjd | | | | \} | S) Nrrrsrrrrr%sLRNGradrTrr)rrrrrrrr r!r[rrPrOrJs rQrr{sbL""<@, \ D   T6 *$ ] E   eW -% \ D   T6 *$66 [R^7_adgngsgsu|vFvFHOHWHWgZ\c\k\kl'9-6*; \{L9, L&$$W &   Z&   ]Al#)s ?' ""$ lFG5 (' .r\ TV_MaxPool_Tctjxstj}|j}|jr% t j |d||d|d|d|d|d|} | St|ttfst!d |z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|g}t|ttfst!d |z|D cgc]} t#j$| d}} |d }t#j&|d}t)j*d||||||| \} } } }|dd} t#j,rd| j/dd| j1dd| j1dd| j1dd| j1dd| j1df }| j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||S#t j$rYJwxYwcc} wcc} wcc} w)aPerforms max pooling on the input. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`, `int32`, `int64`, `uint8`, `int16`, `int8`, `uint16`, `qint8`. 4-D input to pool over. ksize: A list of `ints` that has length `>= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. explicit_paddings: An optional list of `ints`. Defaults to `[]`. data_format: An optional `string` from: `"NHWC", "NCHW", "NCHW_VECT_C"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. MaxPoolrerfr`rraNrerfr`rrar r!Expected list for 'strides' argument to 'max_pool' Op, not %r.HExpected list for 'explicit_paddings' argument to 'max_pool' Op, not %r.r^)rrerfr`rrar r%)r&rr'r(rr)r,r-r.r/r0max_pool_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrG)rrerfr`rrar rHrIrJrKrorLrMrNrOrPs rQmax_poolrYs2    0h..0$ #\\  11 iugui7/1B{$gn ED%= )  !#( ) **5: :b8  R ) :% : GdE] +  !#* + ,,9@ @"X  r9 - @' @   gy 1' %e} 5  !#4 5 66M^^bx((-@A^^K!!+}=+'88eWg%6*7!QX QK' ""$3%%c*GS\\'5Ji0)ll9%':ll./ll=) +F ::L <2 (' .a  & &- ##At,,  # #    $ ugw-;  # #    ; A_sB#H: J2J74J<:J I((JJJJ/.J/zraw_ops.MaxPoolct|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|g}t|ttfstd|z|Dcgc]}t j |d}}|d}t j |d }t j|g|tjtjtjtjtjtjtjtj tj"tj$tj&g tj\} \}|g} d | d|d|d|d|d |f } t j(d d | | || } t j*rt j,d| | | | \} | Scc}wcc}wcc}w)Nrrerrfr`rrr^rar%sMaxPoolrsrTr)rkrlrmr5r;r<rnrVrWrXrYrZrurrrrrrrrrArG) rrerfr`rrar r!ror[rPrOrJs rQrrs ED%= )  !#( ) **5: :b8  R ) :% : GdE] +  !#* + ,,9@ @"X  r9 - @' @   gy 1' %e} 5  !#4 5 66M^^bx((-@A^^K!!+}=+55ugsW\\SZScSceletetv}wFwFHOHUHUW^WdWdfmfsfsu|uBuBDKDPDPRYR`R`biboboErt{tCtCD'8E, '5)Wi  1=+ O&   ZITV_MaxPool3D_Tctjxstj}|j}|jr# t j |d||d|d|d|d| }|St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}t)j*d|||||| \} } } } | dd}t#j,r{d| j/dd| j/dd| j/dd| j/dd | j1d f }| j2}t#j4d||||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t|||||||S#t j$rYwxYwcc} wcc} w) aPerforms 3D max pooling on the input. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. Shape `[batch, depth, rows, cols, channels]` tensor to pool over. ksize: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. MaxPool3Drerfr`raNrg>Expected list for 'ksize' argument to 'max_pool3d' Op, not %r.@Expected list for 'strides' argument to 'max_pool3d' Op, not %r.rxr}r%)r&rr'r(rr)r,r-r.r/r0max_pool3d_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrs rQ max_pool3drsk2    0h..0$ #\\ 11 k4 77M;8gn ED%= )  #%* + ,,5: :b8  R ) :% : GdE] +  #%, - ..9@ @"X  r9 - @' @   gy 1'K!!+}=+'885w$+DJ!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L \674 (' .M  & &- ##At,,  # #    & ugw!$88  # #    ; Arqzraw_ops.MaxPool3Dc t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j|g|tjtjtjg\}\}|g} d|d|d|d|d|f } t jd d | | || } t jrt jd | | | | \} | Scc}wcc}w) Nrrerrfr`rxrar%s MaxPool3DrsrTrrkrlrmr5r;r<rnrVrWrXrYrZrrArGrs rQrr!sw ED%= )  #%* + ,,5: :b8  R ) :% : GdE] +  #%, - ..9@ @"X  r9 - @' @   gy 1'K!!+}=+55ugsW\\SZScSceletetDwx'8E, UIw 7c7 ,&   \1\#)s ?' ""$ \674 (' .) ; As E%0E*TV_MaxPool3DGrad_TTV_MaxPool3DGrad_TInputctjxstj}|j} | jr% t j |d||||d|d|d|d|} | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}t)j*d|||||||| \} } }}|dd} t#j,rd|j/dd|j/dd|j/dd|j/dd |j1d d |j1d f }|j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYwcc} wcc} w)aComputes gradients of 3D max pooling function. Args: orig_input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. The original input tensor. orig_output: A `Tensor`. Must have the same type as `orig_input`. The original output tensor. grad: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`. Output backprop of shape `[batch, depth, rows, cols, channels]`. ksize: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `grad`. MaxPool3DGradrerfr`raNrgCExpected list for 'ksize' argument to 'max_pool3d_grad' Op, not %r.EExpected list for 'strides' argument to 'max_pool3d_grad' Op, not %r.rxrrrrerfr`rar r%TInput)r&rr'r(rr)r,r-r.r/r0max_pool3d_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrrrrerfr`rar rHrIrJrKrorLrMrNrOrPs rQmax_pool3d_gradr@s:    0h..0$ #\\  11 otZdG y'9g}gn ED%= )  (*/ 0 115: :b8  R ) :% : GdE] +  (*1 2 339@ @"X  r9 - @' @   gy 1'K!!+}=+'88JK"%!(kN!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %x1C1CH1MOF::L vw8 (' .O  & &- ##At,,  # #    + k4ug{4II  # #    ; A<#G0 I)I.0H7HH76H7;II&%I&zraw_ops.MaxPool3DGradc t|ttfstd|z|D cgc]} t j | d}} t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j |d}t j|g|tjtjtjgtj\} \}t j||g|tjtjtjgtj\} } | \}}|||g} d|d|d|d|d| d | f }t jd d | ||| }t jrt jd | |||\}|Scc} wcc} w)Nrrerrfr`rxrar%rs MaxPool3DGradrsrTrr)rrrrerfr`rar r!ror[ _attr_TInput_inputs_TInputrPrOrJs rQrrs  ED%= )  (*/ 0 115: :b8  R ) :% : GdE] +  (*1 2 339@ @"X  r9 - @' @   gy 1'K!!+}=+44dVS7<= 5`. 1-D tensor of length 5. The size of the window for each dimension of the input tensor. Must have `ksize[0] = ksize[4] = 1`. strides: A list of `ints` that has length `>= 5`. 1-D tensor of length 5. The stride of the sliding window for each dimension of `input`. Must have `strides[0] = strides[4] = 1`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NDHWC", "NCDHW"`. Defaults to `"NDHWC"`. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `orig_input`. MaxPool3DGradGradrerfr`raNrgHExpected list for 'ksize' argument to 'max_pool3d_grad_grad' Op, not %r.JExpected list for 'strides' argument to 'max_pool3d_grad_grad' Op, not %r.rxrr%)r&rr'r(rr)r,r-r.r/r0#max_pool3d_grad_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrs rQmax_pool3d_grad_gradrs|:    0h..0$ #\\  11 !4[$ 7Iw gn ED%= )  -/4 5 665: :b8  R ) :% : GdE] +  -/6 7 889@ @"X  r9 - @' @   gy 1'K!!+}=+'88 "&eW%,+"& (!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L \67< (' .Q  & &- ##At,,  # #    0 k4ug{4II  # #    ; A<#G IIH&2H  H&%H&*H>>IIzraw_ops.MaxPool3DGradGradc t|ttfstd|z|D cgc]} t j | d}} t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j |d}t j|||g|tjtjtjtjtjtjtjtj tj"tj$tj&tj(g \} } | \}}}|||g} d|d|d|d|d| f } t j*d d | | || }t j,rt j.d | | ||\}|Scc} wcc} w) Nrrerrfr`rxrar%sMaxPool3DGradGradrsrTrrrrrrerfr`rar r!ror[rrPrOrJs rQrrsa ED%= )  -/4 5 665: :b8  R ) :% : GdE] +  -/6 7 889@ @"X  r9 - @' @   gy 1'K!!+}=+66 KQU7VX[^e^m^movo~o~AHANANPWP]P]_f_l_lnunznz|C|I|IKRK[K[]d]k]kmtmymy{B{I{IKRKYKY^\]'9$-!:{Dk40, UIw 7c7 ,&   11\#)s ?' ""$ \67< (' .+ ; A G40G9TV_MaxPoolGrad_Tc tjxstj} | j} | jr' t j | d||||d|d|d|d|d|} | St|ttfst!d |z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|g}t|ttfst!d |z|D cgc]} t#j$| d}} |d }t#j&|d}t)j*d||||||||| \}}}}|dd} t#j,rd|j/dd|j/dd|j/dd|j/dd|j/dd|j1df }|j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||||  S#t j$rYNwxYwcc} wcc} wcc} w)aComputes gradients of the maxpooling function. Args: orig_input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The original input tensor. orig_output: A `Tensor`. Must have the same type as `orig_input`. The original output tensor. grad: A `Tensor`. Must have the same type as `orig_input`. 4-D. Gradients w.r.t. the output of `max_pool`. ksize: A list of `ints` that has length `>= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID", "EXPLICIT"`. The type of padding algorithm to use. explicit_paddings: An optional list of `ints`. Defaults to `[]`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `orig_input`. MaxPoolGradrerfr`rraNrAExpected list for 'ksize' argument to 'max_pool_grad' Op, not %r.CExpected list for 'strides' argument to 'max_pool_grad' Op, not %r.MExpected list for 'explicit_paddings' argument to 'max_pool_grad' Op, not %r.r^) rrrrerfr`rrar r%)r&rr'r(rr)r,r-r.r/r0max_pool_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrG)rrrrerfr`rrar rHrIrJrKrorLrMrNrOrPs rQ max_pool_gradr$s:    0h..0$ #\\  11 mT:{D' y'9g7J=+7gn ED%= )  &(- . //5: :b8  R ) :% : GdE] +  &(/ 0 119@ @"X  r9 - @' @   gy 1' %e} 5  &(9 : ;;M^^bx((-@A^^K!!+}=+'88*+ w&:K#.T ;!QX QK' ""$s||G,ill9%y#,,y2I!3<<0C#DS\\-8#  % 'F ::L |VW6 (' .c  & &- ##At,,  # #    ) k4ug->!$88  # #    ; A_sB%H>J8J=6K>JI,,JJ JJ54J5zraw_ops.MaxPoolGradc t|ttfstd|z|D cgc]} t j | d}} t|ttfstd|z|D cgc]} t j | d}} t j |d}|g}t|ttfstd|z|D cgc]} t j | d}} |d}t j |d }t j|||g| tjtjtjtjtjtjtjtj tj"tj$tj&tj(g tj\} } | \}}}|||g} d|d|d|d|d |d | f }t j*d d | || | }t j,rt j.d| |||\}|Scc} wcc} wcc} w)Nrrerrfr`rrr^rar%s MaxPoolGradrsrTrr)rrrrerfr`rrar r!ror[rrPrOrJs rQrrs ED%= )  &(- . //5: :b8  R ) :% : GdE] +  &(/ 0 119@ @"X  r9 - @' @   gy 1' %e} 5  &(9 : ;;M^^bx((-@A^^K!!+}=+66 KQU7VX[^e^m^movo~o~AHANANPWP]P]_f_l_lnunznz|C|I|IKRK[K[]d]k]kmtmymy{B{I{IKRKYKY^\^e^m^mn'9$-!:{Dk40, UIw 7(-c  &   ^Q|#)s ?' ""$ |VW6 (' .; ; A_sI0IITV_MaxPoolGradGrad_Tctjxstj}|j} | jr% t j |d||||d|d|d|d|} | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j&|d}t)j*d|||||||| \} } }}|dd} t#j,r{d|j/dd|j/dd|j/dd|j/dd |j1d f }|j2}t#j4d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYwcc} wcc} w) aeComputes second-order gradients of the maxpooling function. Args: orig_input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The original input tensor. orig_output: A `Tensor`. Must have the same type as `orig_input`. The original output tensor. grad: A `Tensor`. Must have the same type as `orig_input`. 4-D. Gradients of gradients w.r.t. the input of `max_pool`. ksize: A list of `ints` that has length `>= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `orig_input`. MaxPoolGradGradrerfr`raNrgFExpected list for 'ksize' argument to 'max_pool_grad_grad' Op, not %r.HExpected list for 'strides' argument to 'max_pool_grad_grad' Op, not %r.r^rr%)r&rr'r(rr)r,r-r.r/r0!max_pool_grad_grad_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArCrErFrGrs rQmax_pool_grad_gradrs|8    0h..0$ #\\  11 z;g y'9g}gn ED%= )  +-2 3 445: :b8  R ) :% : GdE] +  +-4 5 669@ @"X  r9 - @' @   gy 1'K!!+}=+'88jk $E7#* $ &!QX QK' ""$s||G,ill9%y#,,y2IS\\-8#  %'F::L <: (' .Q  & &- ##At,,  # #    . k4ug{4II  # #    ; Arzraw_ops.MaxPoolGradGradc t|ttfstd|z|D cgc]} t j | d}} t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j |d}t j|||g|tjtjtjtjtjtjtjtj tj"tj$tj&tj(g \} } | \}}}|||g} d|d|d|d|d| f } t j*d d | | || }t j,rt j.d | | ||\}|Scc} wcc} w) Nrrerrfr`r^rar%sMaxPoolGradGradrsrTrrrs rQrrsa ED%= )  +-2 3 445: :b8  R ) :% : GdE] +  +-4 5 669@ @"X  r9 - @' @   gy 1'K!!+}=+66 KQU7VX[^e^m^movo~o~AHANANPWP]P]_f_l_lnunznz|C|I|IKRK[K[]d]k]kmtmymy{B{I{IKRKYKY^\]'9$-!:{Dk40, UIw 7c7 ,&   /<#)s ?' ""$ <: (' .+ ; ArTV_MaxPoolGradGradV2_TrerfcLtjxstj}|j} | jr# t j |d||||||d|d| } | Stj|d}|d}tj|d}tj d|||||||| \} } } }|dd} tj"rYd| j%dd| j%dd| j'df}| j(}tj*d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rY@wxYw) aKComputes second-order gradients of the maxpooling function. Args: orig_input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The original input tensor. orig_output: A `Tensor`. Must have the same type as `orig_input`. The original output tensor. grad: A `Tensor`. Must have the same type as `orig_input`. 4-D. Gradients of gradients w.r.t. the input of `max_pool`. ksize: A `Tensor` of type `int32`. The size of the window for each dimension of the input tensor. strides: A `Tensor` of type `int32`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `orig_input`. MaxPoolGradGradV2r`raNr`rar r!r^rr%)r&rr'r(rr)r,r-r.r/r0$max_pool_grad_grad_v2_eager_fallbackr4r;rnr?r@rArCrErFrGrrrrerfr`rar rHrIrJrKrLrMrNrOrPs rQmax_pool_grad_grad_v2rs8    0h..0$ #\\ 11 !4[$G]KAgn   gy 1'K!!+}=+'88 "&eW%,+"& (!QX QK' ""$i0-ll=)30B0B30GIF::L \67< (' .9  & &- ##At,,  # #    1 k4!$88  # #   0!D--E4EE43E48F F#"F#zraw_ops.MaxPoolGradGradV2c Xtj|d}|d}tj|d}tj|||g|tjtj tj tjtjtjtjtjtjtjtjtjg \} } | \}}}t!j"|tj }t!j"|tj }|||||g} d|d|d| f} tj$dd| | ||} tj&rtj(d| | | | \} | S) Nr`r^rar%sMaxPoolGradGradV2rsrTrr;rnrVrWrZrurrrrrrYrrXrrr.rrrArGrrrrerfr`rar r!r[rrPrOrJs rQrr]s   gy 1'K!!+}=+66 KQU7VX[^e^m^movo~o~AHANANPWP]P]_f_l_lnunznz|C|I|IKRK[K[]d]k]kmtmymy{B{I{IKRKYKY^\]'9$-!:{D   6%  " "7GMM :'k4@, w {C I&   11\#)s ?' ""$ \67< (' .r\$TV_MaxPoolGradGradWithArgmax_TargmaxTV_MaxPoolGradGradWithArgmax_Targmaxinclude_batch_in_indexctjxstj}|j} | jr% t j |d||||d|d|d|d|} | St|ttfst!d|z|D cgc]} t#j$| d}} t|ttfst!d |z|D cgc]} t#j$| d}} t#j&|d}|d }t#j(|d}t+j,d|||||||| \} } }}|dd} t#j.rd|j1dd|j1dd|j1dd|j3dd |j5d d |j5d f }|j6}t#j8d||| | \} | S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||||| S#t j$rYwxYwcc} wcc} w)aComputes second-order gradients of the maxpooling function. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The original input. grad: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the input of `max_pool`. argmax: A `Tensor`. Must be one of the following types: `int32`, `int64`. The indices of the maximum values chosen for each output of `max_pool`. ksize: A list of `ints` that has length `>= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. include_batch_in_index: An optional `bool`. Defaults to `False`. Whether to include batch dimension in flattened index of `argmax`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. MaxPoolGradGradWithArgmaxrerfr`rNrerfr`rr r!RExpected list for 'ksize' argument to 'max_pool_grad_grad_with_argmax' Op, not %r.TExpected list for 'strides' argument to 'max_pool_grad_grad_with_argmax' Op, not %r.Frrrrerfr`rr Targmaxr%)r&rr'r(rr)r,r-r.r/r0-max_pool_grad_grad_with_argmax_eager_fallbackr4rkrlrmr5r;r<rnr>r?r@rArCrDrErFrGrrrrerfr`rr rHrIrJrKrorLrMrNrOrPs rQmax_pool_grad_grad_with_argmaxr ts2    0h..0$ #\\  11 )4fg y'9g "8:gn ED%= )  79> ? @@5: :b8  R ) :% : GdE] +  79@ A BB9@ @"X  r9 - @' @   gy 1'#"#--.DF^_'88#5tF+0'-4rVrWrrrZrurrrrYrrXrrrrArGrrrrerfr`rr r!ro _attr_Targmaxr[rrPrOrJs rQrrs ED%= )  79> ? @@5: :b8  R ) :% : GdE] +  79@ A BB9@ @"X  r9 - @' @   gy 1'#"#--.DF^_%<= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. include_batch_in_index: An optional `bool`. Defaults to `False`. Whether to include batch dimension in flattened index of `argmax`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. MaxPoolGradWithArgmaxrerfr`rNrMExpected list for 'ksize' argument to 'max_pool_grad_with_argmax' Op, not %r.OExpected list for 'strides' argument to 'max_pool_grad_with_argmax' Op, not %r.Frrr%)r&rr'r(rr)r,r-r.r/r0(max_pool_grad_with_argmax_eager_fallbackr4rkrlrmr5r;r<rnr>r?r@rArCrDrErFrGr s rQmax_pool_grad_with_argmaxrBs2    0h..0$ #\\  11 %tUD&' y'9g "8:gn ED%= )  249 : ;;5: :b8  R ) :% : GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1'#"#--.DF^_'88u4',g)08N&* ,!QX QK' ""$s||G,ill9%y#,,y2I&  !9:I  +S#2D2DS2I KF ::L vw@ (' .U  & &- ##At,,  # #    5 vUGW!7dNN  # #    ; Arzraw_ops.MaxPoolGradWithArgmaxc rt|ttfstd|z|D cgc]} t j | d}} t|ttfstd|z|D cgc]} t j | d}} t j |d}|d}t j|d}t j|g|tjtjg\} \}t j||g|tjtjtjtjtjtj tjtj"tj$tj&tj(tj*g \} } | \}}|||g} d|d|d|d|d| d | f }t j,d d | ||| }t j.rt j0d | |||\}|Scc} wcc} w)Nrrerrfr`Frrr%sMaxPoolGradWithArgmaxrsrTrr r s rQrrs ED%= )  249 : ;;5: :b8  R ) :% : GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1'#"#--.DF^_%<= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. Targmax: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`. include_batch_in_index: An optional `bool`. Defaults to `False`. Whether to include batch dimension in flattened index of `argmax`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, argmax). output: A `Tensor`. Has the same type as `input`. argmax: A `Tensor` of type `Targmax`. r%rerfrr`rN)rerfrr`rr r!HExpected list for 'ksize' argument to 'max_pool_with_argmax' Op, not %r.JExpected list for 'strides' argument to 'max_pool_with_argmax' Op, not %r.F)rrerfr`rrr r%)"r&rr'r(rr)_MaxPoolWithArgmaxOutputr+r,r-r.r/r0#max_pool_with_argmax_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r>r?r@rArCrErDrFrG)rrerfr`rrr rHrIrJrKrorLrMrNrOrPs rQmax_pool_with_argmaxr-sD    0h..0$ #\\  11 !4 GY "8:g)..w7g n ED%= )  -/4 5 665: :b8  R ) :% : GdE] +  -/6 7 889@ @"X  r9 - @' @   gy 1' _mmG   w 2'#"#--.DF^_'885w%,g4J"& (!QX QK' ""$s||G,ill9%y  +Yll9%'?  !9:C  % 'F ::L \67< $ * *7 3' .]  & &- ##At,,  # #    0 ugw2H  # #    ; As<8H=!J5(J:=JI++JJJJ21J2zraw_ops.MaxPoolWithArgmaxcbt|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}|tj}t j|d}|d}t j|d}t j|g|tjtjtjtjtj tj"tjtj$tj&tj(tj*tj,g \} \}|g} d|d|d|d|d|d | f } t j.d d | | || } t j0rt j2d | | | t4j7| } | Scc}wcc}w)Nr)rer*rfr`rFrr%sMaxPoolWithArgmaxrSrTr%)rkrlrmr5r;r<rnrWrr{r>rVrZrurrrrrYrrXrrrrArGr+r+) rrerfr`rrr r!ror[rPrOrJs rQr,r,msl ED%= )  -/4 5 665: :b8  R ) :% : GdE] +  -/6 7 889@ @"X  r9 - @' @   gy 1' _mmG   w 2'#"#--.DF^_55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjcpcpryrBrBDKDRDRT[T`T`bibpbpryr@r@ECD'8E, UIw 7I #%;S' K&   11\#)s ?' ""$ \67< $ * *7 3' ./ ; As H'0H,TV_NthElement_Tnreversec tjxstj}|j}|jr t j |d|||d|}|S|d}tj|d}tj d||||\}}} } | dd}tj"rHd| j%dd| j'df} | j(} tj*d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)a>Finds values of the `n`-th order statistic for the last dimension. If the input is a vector (rank-1), finds the entries which is the nth-smallest value in the vector and outputs their values as scalar tensor. For matrices (resp. higher rank input), computes the entries which is the nth-smallest value in each row (resp. vector along the last dimension). Thus, values.shape = input.shape[:-1] Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 1-D or higher with last dimension at least `n+1`. n: A `Tensor` of type `int32`. 0-D. Position of sorted vector to select along the last dimension (along each row for matrices). Valid range of n is `[0, input.shape[:-1])` reverse: An optional `bool`. Defaults to `False`. When set to True, find the nth-largest value in the vector and vice versa. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. NthElementr1N)r1r r!F)rr0r1r r%)r&rr'r(rr)r,r-r.r/r0nth_element_eager_fallbackr4r;r>r?r@rArDrErFrG) rr0r1r rHrIrJrKrLrMrNrOrPs rQ nth_elementr5su2    0h..0$ #\\ 11 lD%Iw@g n _G   w 2''88EQdD!QX QK' ""$++I6  %'F::L lFG5 (' ./  & &- ##At,,  # #    ' G$D::  # #   rzraw_ops.NthElementc|d}tj|d}tj|g|tjtj tj tjtjtjtjtjtjtjtjtjg \}\}t!j"|tj }||g}d|d|f}tj$dd||||}tj&rtj(d||||\}|S)NFr1r%s NthElementrsrTr3)r;r>rVrWrZrurrrrrrYrrXrrr.rrrArG) rr0r1r r!r[rPrOrJs rQr4r4s _G   w 2'55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjcpcpryrBrBDKDRDRT[T`T`bibpbpryr@r@ECD'8E Q .!, wW -&   ]Al#)s ?' ""$ lFG5 (' .r\QuantizedAvgPool)r min_output max_outputTV_QuantizedAvgPool_T min_input max_inputctjxstj}|j}|jr8 t j |d||||d|d|d| } t j| } | St|t t"fst%d|z|D cgc]} t'j(| d}} t|t t"fst%d|z|D cgc]} t'j(| d}} t'j*|d}t-j.d||||||| \} } } }|dd} t'j0rjd | j3d d| j5dd| j5dd| j5df}| j6}t'j8d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||S#tj$rYwxYwcc} wcc} w) aProduces the average pool of the input tensor for quantized types. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. 4-D with shape `[batch, height, width, channels]`. min_input: A `Tensor` of type `float32`. The float value that the lowest quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the highest quantized input value represents. ksize: A list of `ints`. The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. strides: A list of `ints`. The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor`. Has the same type as `input`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. r7rerfr`Nrerfr`r r!FExpected list for 'ksize' argument to 'quantized_avg_pool' Op, not %r.HExpected list for 'strides' argument to 'quantized_avg_pool' Op, not %r.rr;r<rerfr`r r%)r&rr'r(rr)_QuantizedAvgPoolOutputr+r,r-r.r/r0!quantized_avg_pool_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGrr;r<rerfr`r rHrIrJrKrorLrMrNrOrPs rQquantized_avg_poolrE^6    0h..0$ #\\  11  $y)W y'9g7g(--g6g n ED%= )  +-2 3 445: :b8  R ) :% : GdE] +  +-4 5 669@ @"X  r9 - @' @   gy 1''88%9&/ug$+$8!QX QK' ""$3%%c*GS\\'5Ji0)ll9%'F::L L&'; # ) )' 2' .G  & &- ##At,,  # #    . IUG$00  # #    ; A<6GI&IH*HHH"H55I  I zraw_ops.QuantizedAvgPoolc t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}t j|g|tjtjtjtjtjg\} \}tj|tj }tj|tj }|||g} d| d|d|d|f} t j"dd| | || } t j$rt j&d | | | t(j+| } | Scc}wcc}w) Nr?rer@rfr`r%sQuantizedAvgPoolrrTr7)rkrlrmr5r;r<rnrVrWrrrrrr.rrZrrArGrBr+ rr;r<rerfr`r r!ror[rPrOrJs rQrCrC- ED%= )  +-2 3 445: :b8  R ) :% : GdE] +  +-4 5 669@ @"X  r9 - @' @   gy 1'55ugsW]]T[TbTbdkdrdrt{uCuCELETETEWX'8E$$Y@)$$Y@)I., '5)Wi  &   0!L#)s ?' ""$ L&'; # ) )' 2' .' ; A G0G)QuantizedBatchNormWithGlobalNormalization)result result_min result_max3TV_QuantizedBatchNormWithGlobalNormalization_Tinput5TV_QuantizedBatchNormWithGlobalNormalization_out_typet_mint_maxm_minm_maxv_minv_maxbeta_minbeta_max gamma_min gamma_maxout_typecjtjxstj}|j}|jrD t j |d||||||||||| | | | | |d|d|d|}t j|}|Stj |d}tj"|d}tj$|d}t'j( did|d|d |d |d |d |d |d|d|d| d| d| d| d| d|d|d|d|d|\}}}}|dd}tj*rjd|j-dd|j-dd|j/dd|j1df}|j2}tj4d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | ||||||S#tj$rYwxYw)a Quantized Batch normalization. This op is deprecated and will be removed in the future. Prefer `tf.nn.batch_normalization`. Args: t: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A 4D input Tensor. t_min: A `Tensor` of type `float32`. The value represented by the lowest quantized input. t_max: A `Tensor` of type `float32`. The value represented by the highest quantized input. m: A `Tensor`. Must have the same type as `t`. A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof. m_min: A `Tensor` of type `float32`. The value represented by the lowest quantized mean. m_max: A `Tensor` of type `float32`. The value represented by the highest quantized mean. v: A `Tensor`. Must have the same type as `t`. A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof. v_min: A `Tensor` of type `float32`. The value represented by the lowest quantized variance. v_max: A `Tensor` of type `float32`. The value represented by the highest quantized variance. beta: A `Tensor`. Must have the same type as `t`. A 1D beta Tensor with size matching the last dimension of t. An offset to be added to the normalized tensor. beta_min: A `Tensor` of type `float32`. The value represented by the lowest quantized offset. beta_max: A `Tensor` of type `float32`. The value represented by the highest quantized offset. gamma: A `Tensor`. Must have the same type as `t`. A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this tensor will be multiplied with the normalized tensor. gamma_min: A `Tensor` of type `float32`. The value represented by the lowest quantized gamma. gamma_max: A `Tensor` of type `float32`. The value represented by the highest quantized gamma. out_type: A `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. variance_epsilon: A `float`. A small float number to avoid dividing by 0. scale_after_normalization: A `bool`. A bool indicating whether the resulted tensor needs to be multiplied with gamma. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (result, result_min, result_max). result: A `Tensor` of type `out_type`. result_min: A `Tensor` of type `float32`. result_max: A `Tensor` of type `float32`. rLr\rrN)r\rrr r!rrRrSrrTrUrrVrWrrXrYrrZr[r Tinput)rL)r&rr'r(rr)0_QuantizedBatchNormWithGlobalNormalizationOutputr+r,r-r.r/r0=quantized_batch_norm_with_global_normalization_eager_fallbackr4r;r{r=r>r?r@rArErCrDrFrG)rrRrSrrTrUrrVrWrrXrYrrZr[r\rrr rHrIrJrKrLrMrNrOrPs rQ.quantized_batch_norm_with_global_normalizationraOst    0h..0$ #\\  11 94E q%5%x y)Z;M5! #g AFFwOg n  * 5((()9;MN&001JLgh'883@78@@E@;@@DE@rVrWrrrrrr.rrZrrArGr_r+)rrRrSrrTrUrrVrWrrXrYrrZr[r\rrr r! _attr_Tinput_inputs_TinputrPrOrJs rQr`r`sH   * 5((()9;MN&001JLgh!)!@!@!Q4QVAWY\_f_l_lnun|n|FMMOVO]O]_f_n_n_q"r,)1aD%   8%   8%   8%   8%   8%   8%  # #Hgoo >(  # #Hgoo >($$Y@)$$Y@)UE1eUAueT8U]_dfoqz{, lJ:L/1J L&   I1$0C"& ('""$ 3\67T < B B7 K' .r\QuantizedBiasAdd)rmin_outmax_outTV_QuantizedBiasAdd_T1TV_QuantizedBiasAdd_T2TV_QuantizedBiasAdd_out_typemin_biasmax_biasc btjxstj}|j} | jr7 t j |d|||||||d| } t j| } | Stj |d}t#j$d|||||||| \} } } }|dd} tj&rYd| j)dd| j)dd| j)df}| j*}tj,d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||| S#tj$rY7wxYw)aAdds Tensor 'bias' to Tensor 'input' for Quantized types. Broadcasts the values of bias on dimensions 0..N-2 of 'input'. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A 1D bias Tensor with size matching the last dimension of 'input'. min_input: A `Tensor` of type `float32`. The float value that the lowest quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the highest quantized input value represents. min_bias: A `Tensor` of type `float32`. The float value that the lowest quantized bias value represents. max_bias: A `Tensor` of type `float32`. The float value that the highest quantized bias value represents. out_type: A `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_out, max_out). output: A `Tensor` of type `out_type`. min_out: A `Tensor` of type `float32`. max_out: A `Tensor` of type `float32`. rer\Nr\r r!)rrr;r<rkrlr\r T1T2)r&rr'r(rr)_QuantizedBiasAddOutputr+r,r-r.r/r0!quantized_bias_add_eager_fallbackr4r;r{r?r@rArErFrG)rrr;r<rkrlr\r rHrIrJrKrLrMrNrOrPs rQquantized_bias_addrss6    0h..0$ #\\  11  $tY (J2g(--g6g n  * 5('88%di&/(%-tM!QX QK' ""$C&&t,dC4F4Ft4L#,,Z8:F::L L&'; # ) )' 2' .1  & &- ##At,,  # #    . y)Xx$D22  # #   s05D88E? E&&E?>E?FF.-F.zraw_ops.QuantizedBiasAddc tj|d}tj|g|tjtj tj tjtjg\} \}tj|g|tjtj tj tjtjg\} \}tj|tj}tj|tj}tj|tj}tj|tj}||||||g} d| d| d|f} tjdd| | ||} tjrtjd| | | tj!| } | S)Nr\rorpsQuantizedBiasAddrrTre)r;r{rVrWrrrrrr.rrZrrArGrqr+)rrr;r<rkrlr\r r!_attr_T1_attr_T2rPrOrJs rQrrrr*s   * 5(66wgmmU\UcUcelesesu|vDvDFMFUFUFXY(HU55tfcGMMSZSaSacjcqcqsztBtBDKDSDSDVW(GT$$Y@)$$Y@)  # #Hgoo >(  # #Hgoo >(y)XxH, (D(J A&   0!L#)s ?' ""$ L&'; # ) )' 2' .r\QuantizedConv2DTV_QuantizedConv2D_TinputTV_QuantizedConv2D_TfilterTV_QuantizedConv2D_out_type min_filter max_filterc ltjxstj} | j} | jr= t j | d| ||||||d|d|d|d| } t j| } | St|t t"fst%d|z|Dcgc]}t'j(|d}}t'j*|d}|t,j.}t'j0|d}| gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | \}}}}|dd} t'j6rd |j9d d |j9d d|j9dd|j;dd|j;dd|j;df }|j<}t'j>d||| t j| } | S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | |  S#tj$rY,wxYwcc}wcc}w)apComputes a 2D convolution given quantized 4D input and filter tensors. The inputs are quantized tensors where the lowest value represents the real number of the associated minimum, and the highest represents the maximum. This means that you can only interpret the quantized output in the same way, by taking the returned minimum and maximum values into account. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter's input_depth dimension must match input's depth dimensions. min_input: A `Tensor` of type `float32`. The float value that the lowest quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the highest quantized input value represents. min_filter: A `Tensor` of type `float32`. The float value that the lowest quantized filter value represents. max_filter: A `Tensor` of type `float32`. The float value that the highest quantized filter value represents. strides: A list of `ints`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rwr\rfr`rNr\rfr`rr r!FExpected list for 'strides' argument to 'quantized_conv2d' Op, not %r.rHExpected list for 'dilations' argument to 'quantized_conv2d' Op, not %r. rrr;r<r{r|rfr`r\rr r^Tfilter) r&rr'r(rr)_QuantizedConv2DOutputr+r,r-r.r/r0quantized_conv2d_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrrr;r<r{r|rfr`r\rr rHrIrJrKrorLrMrNrOrPs rQquantized_conv2drEsR    0h..0$ #\\  11 ufiJ Hi7K4g',,W5g n GdE] +  )+2 3 449@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  )+4 5 66=FFbx  [1F)F'88v%.:&0'#*X%.T ;!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4 6F ::L <: " ( ( 1' .[  & &- ##At,,  # #    , Iz:WgDd44  # #    AG<;H0$J,/J10I7II76I7;JJ)(J)zraw_ops.QuantizedConv2Dc bt|ttfstd|z|D cgc]} t j | d}} t j |d}|tj}t j|d}| gd} t| ttfstd| z| D cgc]} t j | d} } t j|g| tjtjtjtjtjg\} \}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||g}d| d |d|d|d|d| f }t j$d d ||| | }t j&rt j(d |||t*j-|}|Scc} wcc} w)Nrrfr`r\rrrr^rsQuantizedConv2DrrTrw)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rrr;r<r{r|rfr`r\rr r!rorc _attr_TfilterrPrOrJs rQrrs GdE] +  )+2 3 449@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  )+4 5 66=FFbx  [1F)F#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | | |  S#tj$rYwxYwcc}wcc}wcc}w)aTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`r padding_listNr\rfr`rrr r!OExpected list for 'strides' argument to 'quantized_conv2d_and_relu' Op, not %r.rQExpected list for 'dilations' argument to 'quantized_conv2d_and_relu' Op, not %r.TExpected list for 'padding_list' argument to 'quantized_conv2d_and_relu' Op, not %r.) rrr;r<r{r|rfr`r\rrr r^r) r&rr'r(rr)_QuantizedConv2DAndReluOutputr+r,r-r.r/r0(quantized_conv2d_and_relu_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrG)rrr;r<r{r|rfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQquantized_conv2d_and_relursG0    0h..0$ #\\  11 &eVY:z:xG[)^ g .33G ??=FFbx  [1F)FL L4- 0  24@ A BBCOOB(##B7O,O'88 f,5-7-7*1H,5/;$H!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L  ,A ) / / 8' .o  & &- ##At,,  # #    5 Iz:WgLtOO  # #    AGPsB=J&L 1L<LK"J==KKK22L L zraw_ops.QuantizedConv2DAndReluc t|ttfstd|z|D cgc]} t j | d}} t j |d}|tj}t j|d}| gd} t| ttfstd| z| D cgc]} t j | d} } | g} t| ttfstd| z| D cgc]} t j | d } } t j|g| tjtjtjtjtjg\}\}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||g}d |d |d|d|d|d| d | f}t j$d d ||| | }t j&rt j(d|||t*j-|}|Scc} wcc} wcc} w)Nrrfr`r\rrrrrr^rsQuantizedConv2DAndRelurrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+)rrr;r<r{r|rfr`r\rrr r!rorcrrPrOrJs rQrr4 s GdE] +  24; < ==9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  24= > ??=FFbx  [1F)FL L4- 0  24@ A BBCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<#QuantizedConv2DAndReluAndRequantize-TV_QuantizedConv2DAndReluAndRequantize_Tinput.TV_QuantizedConv2DAndReluAndRequantize_Tfilter/TV_QuantizedConv2DAndReluAndRequantize_out_typemin_freezed_outputmax_freezed_outputcBtjxstj}|j}|jrA t j |d| ||||||||d| d|d| d| d| }t j|}|St|t t"fst%d |z|Dcgc]}t'j(|d}}t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | | | | \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t||||||||| || | | | |S#tj$rYwxYwcc}wcc}wcc}w)apTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNr^Expected list for 'strides' argument to 'quantized_conv2d_and_relu_and_requantize' Op, not %r.r`Expected list for 'dilations' argument to 'quantized_conv2d_and_relu_and_requantize' Op, not %r.cExpected list for 'padding_list' argument to 'quantized_conv2d_and_relu_and_requantize' Op, not %r.rrr;r<r{r|rrrfr`r\rrr r^r) r&rr'r(rr)*_QuantizedConv2DAndReluAndRequantizeOutputr+r,r-r.r/r07quantized_conv2d_and_relu_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrrr;r<r{r|rrrfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQ(quantized_conv2d_and_relu_and_requantizerh sc4    0h..0$ #\\  11 3T5&9j*6HJ)W7KN g ;@@Ig n GdE] +  ACJ K LL9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  ACL M NN=FFbx  [1F)FL L4- 0  ACO P QQCOOB(##B7O,O'88-U69B9B:D:DBTBT7>7>8@9B* ,F ::L -|VWN 6 < LK&KKKK88LLz+raw_ops.QuantizedConv2DAndReluAndRequantizect|ttfstd|z|Dcgc]}t j |d}}t j | d} | tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||||g}d |d |d| d|d| d| d | f}t j$d d |||| }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rs#QuantizedConv2DAndReluAndRequantizerrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rrr;r<r{r|rrrfr`r\rrr r!rorcrrPrOrJs rQrr s< GdE] +  ACJ K LL9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  ACL M NN=FFbx  [1F)FL L4- 0  ACO P QQCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<4M?MQuantizedConv2DAndRequantize&TV_QuantizedConv2DAndRequantize_Tinput'TV_QuantizedConv2DAndRequantize_Tfilter(TV_QuantizedConv2DAndRequantize_out_typecBtjxstj}|j}|jrA t j |d| ||||||||d| d|d| d| d| }t j|}|St|t t"fst%d |z|Dcgc]}t'j(|d}}t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | | | | \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t||||||||| || | | | |S#tj$rYwxYwcc}wcc}wcc}w)aoTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint8`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNrUExpected list for 'strides' argument to 'quantized_conv2d_and_requantize' Op, not %r.rWExpected list for 'dilations' argument to 'quantized_conv2d_and_requantize' Op, not %r.ZExpected list for 'padding_list' argument to 'quantized_conv2d_and_requantize' Op, not %r.rr^r) r&rr'r(rr)#_QuantizedConv2DAndRequantizeOutputr+r,r-r.r/r0.quantized_conv2d_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQquantized_conv2d_and_requantizer!s_4    0h..0$ #\\  11 ,dE69:z+=J)W7KN g 499'Bg n GdE] +  8:A B CC9@ @"X  r9 - @' @   gy 1' }}H   * 5(I Ie} -  8:C D EE=FFbx  [1F)FL L4- 0  8:F G HHCOOB(##B7O,O'88&eF2;2;3=3=;M;M07192;5A N!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L & fgG / 5 5g >' .y  & &- ##At,,  # #    ; Iz: 087i#$D ::  # #    AGPrz$raw_ops.QuantizedConv2DAndRequantizect|ttfstd|z|Dcgc]}t j |d}}t j | d} | tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||||g}d |d |d| d|d| d| d | f}t j$d d |||| }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rsQuantizedConv2DAndRequantizerrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rs rQrrn!s9 GdE] +  8:A B CC9@ @"X  r9 - @' @   gy 1' }}H   * 5(I Ie} -  8:C D EE=FFbx  [1F)FL L4- 0  8:F G HHCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<' .M AGPrQuantizedConv2DPerChannel#TV_QuantizedConv2DPerChannel_Tinput$TV_QuantizedConv2DPerChannel_Tfilter%TV_QuantizedConv2DPerChannel_out_typec ltjxstj} | j} | jr= t j | d| ||||||d|d|d|d| } t j| } | St|t t"fst%d|z|Dcgc]}t'j(|d}}t'j*|d}|t,j.}t'j0|d}| gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | \}}}}|dd} t'j6rd |j9d d |j9d d|j9dd|j;dd|j;dd|j;df }|j<}t'j>d||| t j| } | S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | |  S#tj$rY,wxYwcc}wcc}w)aOComputes QuantizedConv2D per channel. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original input tensor. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original filter tensor. min_input: A `Tensor` of type `float32`. The minimum value of the input tensor max_input: A `Tensor` of type `float32`. The maximum value of the input tensor. min_filter: A `Tensor` of type `float32`. The minimum value of the filter tensor. max_filter: A `Tensor` of type `float32`. The maximum value of the filter tensor. strides: A list of `ints`. list of stride values. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. The quantized type of output tensor that needs to be converted. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. list of dilation values. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rNr~RExpected list for 'strides' argument to 'quantized_conv2d_per_channel' Op, not %r.rTExpected list for 'dilations' argument to 'quantized_conv2d_per_channel' Op, not %r.rr^r) r&rr'r(rr) _QuantizedConv2DPerChannelOutputr+r,r-r.r/r0+quantized_conv2d_per_channel_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQquantized_conv2d_per_channelr!s>    0h..0$ #\\  11 )4 :z:xG[)=g166w?g n GdE] +  57> ? @@9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  57@ A BB=FFbx  [1F)F'88#5/8I0:0:G-4x/8t E!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4 6F ::L #\67D , 2 27 ;' .]  & &- ##At,,  # #    8 Iz:WgDd44  # #    AGrz!raw_ops.QuantizedConv2DPerChannelc bt|ttfstd|z|D cgc]} t j | d}} t j |d}|tj}t j|d}| gd} t| ttfstd| z| D cgc]} t j | d} } t j|g| tjtjtjtjtjg\} \}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||g}d| d |d|d|d|d| f }t j$d d ||| | }t j&rt j(d |||t*j-|}|Scc} wcc} w)Nrrfr`r\rrrr^rsQuantizedConv2DPerChannelrrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rs rQrr"s GdE] +  57> ? @@9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  57@ A BB=FFbx  [1F)F#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||| ||| | | | S#tj$rYwxYwcc}wcc}wcc}w),TODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor` of type `float32`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNrPExpected list for 'strides' argument to 'quantized_conv2d_with_bias' Op, not %r.rRExpected list for 'dilations' argument to 'quantized_conv2d_with_bias' Op, not %r.UExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias' Op, not %r. rrrr;r<r{r|rfr`r\rrr r^r) r&rr'r(rr)_QuantizedConv2DWithBiasOutputr+r,r-r.r/r0)quantized_conv2d_with_bias_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrrrr;r<r{r|rfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQquantized_conv2d_with_biasr,"sO2    0h..0$ #\\  11 'ufdI:z:xG[)^ g /44W=g n GdE] +  35< = >>9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  35> ? @@=FFbx  [1F)FL L4- 0  35A B CCCOOB(##B7O,O'88!vD-6).8.8'+2X-60<4I!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L !<B * 0 0 9' .o  & &- ##At,,  # #    6 y)ZWgLtOO  # #    AGPB>J'L2L=LK$J??KKK55L  L zraw_ops.QuantizedConv2DWithBiascPt|ttfstd|z|Dcgc]}t j |d}}t j |d}| tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g| tjtjtjtjtjg\}\}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}|||||||g}d |d |d| d|d|d| d | f}t j$d d ||| | }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rsQuantizedConv2DWithBiasrrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rrrr;r<r{r|rfr`r\rrr r!rorcrrPrOrJs rQrr"s GdE] +  35< = >>9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  35> ? @@=FFbx  [1F)FL L4- 0  35A B CCCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||| ||| | | | S#tj$rYwxYwcc}wcc}wcc}w)rrr\rfr`rrNrYExpected list for 'strides' argument to 'quantized_conv2d_with_bias_and_relu' Op, not %r.r[Expected list for 'dilations' argument to 'quantized_conv2d_with_bias_and_relu' Op, not %r.^Expected list for 'padding_list' argument to 'quantized_conv2d_with_bias_and_relu' Op, not %r.rr^r) r&rr'r(rr)%_QuantizedConv2DWithBiasAndReluOutputr+r,r-r.r/r02quantized_conv2d_with_bias_and_relu_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQ#quantized_conv2d_with_bias_and_relur"sU2    0h..0$ #\\  11 .eVT9j*j(7Iw Y &g 6;;GDg n GdE] +  <>E F GG9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  <>G H II=FFbx  [1F)FL L4- 0  <>J K LLCOOB(##B7O,O'88(f/3y4=5?5?2973;4=7C/3 5!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L (,I 1 7 7 @' .u  & &- ##At,,  # #    ? y)ZWgLtOO  # #    AGPrz&raw_ops.QuantizedConv2DWithBiasAndRelucPt|ttfstd|z|Dcgc]}t j |d}}t j |d}| tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g| tjtjtjtjtjg\}\}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}|||||||g}d |d |d| d|d|d| d | f}t j$d d ||| | }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rsQuantizedConv2DWithBiasAndRelurrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrr+rs rQrr$#s GdE] +  <>E F GG9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  <>G H II=FFbx  [1F)FL L4- 0  <>J K LLCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<$0C"& ('""$ (,I 1 7 7 @' .K AGPr+QuantizedConv2DWithBiasAndReluAndRequantize5TV_QuantizedConv2DWithBiasAndReluAndRequantize_Tinput6TV_QuantizedConv2DWithBiasAndReluAndRequantize_Tfilter4TV_QuantizedConv2DWithBiasAndReluAndRequantize_Tbias7TV_QuantizedConv2DWithBiasAndReluAndRequantize_out_typechtjxstj}|j}|jrB t j |d||||||||||d| d| d| d| d| }t j|}|St| t t"fst%d | z| Dcgc]}t'j(|d} }t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4 d|||||||||| | | | | |d \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | ||S#tj$rYwxYwcc}wcc}wcc}w)aTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNrhExpected list for 'strides' argument to 'quantized_conv2d_with_bias_and_relu_and_requantize' Op, not %r.rjExpected list for 'dilations' argument to 'quantized_conv2d_with_bias_and_relu_and_requantize' Op, not %r.mExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias_and_relu_and_requantize' Op, not %r.rrrr;r<r{r|rrrfr`r\rrr r^rTbias)r) r&rr'r(rr)2_QuantizedConv2DWithBiasAndReluAndRequantizeOutputr+r,r-r.r/r0Aquantized_conv2d_with_bias_and_relu_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrrrr;r<r{r|rrrfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQ2quantized_conv2d_with_bias_and_relu_and_requantizerZ#s6    0h..0$ #\\  11 ;T5iJ . H7Iw Y &g CHHQg n GdE] +  KMT U VV9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  KMV W XX=FFbx  [1F)FL L4- 0  KMY Z [[COOB(##B7O,O'885B=B>D<@AJAJBLBLJ\J\?F?F@HAJDP<@B!QX QK' ""$**84i  +W  ):  ,ill9%y#,,y2I3<< 4nll>* ,F::L 5|VWV > D DW M' .C  & &- ##At,,  # #    N y)Z 087i#$D ::  # #    AGPCAJ%)L%4L*?L/%K,8KK,+K,0L L"!L"z3raw_ops.QuantizedConv2DWithBiasAndReluAndRequantizect| ttfstd| z| Dcgc]}t j |d} }t j | d} | tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}t j|g|tjtjg\}\}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}|||||||||g }d |d |d |d| d| d| d| d | f}t j$d d||||}t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rrs+QuantizedConv2DWithBiasAndReluAndRequantizerrTr)rkrlrmr5r;r<rnrWrr{rVrrrrrZr.rrrArGrr+rrrr;r<r{r|rrrfr`r\rrr r!rorcr _attr_TbiasrPrOrJs rQrr#so GdE] +  KMT U VV9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  KMV W XX=FFbx  [1F)FL L4- 0  KMY Z [[COOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%< D DW M' .O AGPM<4N?N$QuantizedConv2DWithBiasAndRequantize.TV_QuantizedConv2DWithBiasAndRequantize_Tinput/TV_QuantizedConv2DWithBiasAndRequantize_Tfilter-TV_QuantizedConv2DWithBiasAndRequantize_Tbias0TV_QuantizedConv2DWithBiasAndRequantize_out_typechtjxstj}|j}|jrB t j |d||||||||||d| d| d| d| d| }t j|}|St| t t"fst%d | z| Dcgc]}t'j(|d} }t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4 d|||||||||| | | | | |d \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | ||S#tj$rYwxYwcc}wcc}wcc}w)aTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint8`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNr_Expected list for 'strides' argument to 'quantized_conv2d_with_bias_and_requantize' Op, not %r.raExpected list for 'dilations' argument to 'quantized_conv2d_with_bias_and_requantize' Op, not %r.dExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias_and_requantize' Op, not %r.rr^rr)r) r&rr'r(rr)+_QuantizedConv2DWithBiasAndRequantizeOutputr+r,r-r.r/r08quantized_conv2d_with_bias_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQ)quantized_conv2d_with_bias_and_requantizer#s6    0h..0$ #\\  11 4dE6 iJ . H7Iw Y &g <AA'Jg n GdE] +  BDK L MM9@ @"X  r9 - @' @   gy 1' }}H   * 5(I Ie} -  BDM N OO=FFbx  [1F)FL L4- 0  BDP Q RRCOOB(##B7O,O'88.;6;F59:C:C;E;ECUCU8?8?9A:C=I59;!QX QK' ""$**84i  +W  ):  ,ill9%y#,,y2I3<< 4nll>* ,F::L . fgO 7 = =g F' .A  & &- ##At,,  # #    E y)Z 087i#$D ::  # #    AGPrz,raw_ops.QuantizedConv2DWithBiasAndRequantizect| ttfstd| z| Dcgc]}t j |d} }t j | d} | tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}t j|g|tjtjg\}\}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}|||||||||g }d |d |d |d| d| d| d| d | f}t j$d d||||}t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nrrfr`r\rrrrrr^rrs$QuantizedConv2DWithBiasAndRequantizerrTr)rkrlrmr5r;r<rnrWrr{rVrrrrrZr.rrrArGrr+rs rQrrk$so GdE] +  BDK L MM9@ @"X  r9 - @' @   gy 1' }}H   * 5(I Ie} -  BDM N OO=FFbx  [1F)FL L4- 0  BDP Q RRCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<TV_QuantizedConv2DWithBiasSignedSumAndReluAndRequantize_Tinput?TV_QuantizedConv2DWithBiasSignedSumAndReluAndRequantize_Tfilter=TV_QuantizedConv2DWithBiasSignedSumAndReluAndRequantize_Tbias@TV_QuantizedConv2DWithBiasSignedSumAndReluAndRequantize_Tsummand@TV_QuantizedConv2DWithBiasSignedSumAndReluAndRequantize_out_typesummand min_summand max_summandctjxstj}|j}|jrE t j |d||||||||||| | | d|d| d| d|d|}t j|}|St| t t"fst%d | z| Dcgc]}t'j(|d} }t'j*| d} |t,j.}t'j0|d}|gd }t|t t"fst%d |z|Dcgc]}t'j(|d}}|g}t|t t"fst%d |z|Dcgc]}t'j(|d}}t3j4 did |d|d|d|d|d|d|d|d|d| d| d| d| d| d|d|d|d|\}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | || | ||||S#tj$rYwxYwcc}wcc}wcc}w)TODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. summand: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_summand: A `Tensor` of type `float32`. max_summand: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNrsExpected list for 'strides' argument to 'quantized_conv2d_with_bias_signed_sum_and_relu_and_requantize' Op, not %r.ruExpected list for 'dilations' argument to 'quantized_conv2d_with_bias_signed_sum_and_relu_and_requantize' Op, not %r.xExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias_signed_sum_and_relu_and_requantize' Op, not %r.rrrr;r<r{r|rrr r r r r^rrTsummand)r) r&rr'r(rr);_QuantizedConv2DWithBiasSignedSumAndReluAndRequantizeOutputr+r,r-r.r/r0Lquantized_conv2d_with_bias_signed_sum_and_relu_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrrrr;r<r{r|rrr r r rfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQ=quantized_conv2d_with_bias_signed_sum_and_relu_and_requantizer$sT<    0h..0$ #\\  11 Dd vtY :z.Z9gyi Gg LQQRYZg n GdE] +  VX_ ` aa9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  VXa b cc=FFbx  [1F)FL L4- 0  VXd e ffCOOB(##B7O,O'88>KFKKGMKFJKKT K KT K LV KLVKTfKTfKIPKMXKMXKIPKIPKJRK KT!K"NZ#K$FJ%K!QX& QK' ""$**84i  +W  ):  ,j  ,ill9%y#,,y2I3<< 4nll>*,F::L > fV]_ G M Mg V' .K  & &- ##At,,  # #    Y y)Z 0'; '7Lt OO  # #    AGPCAK,M"7M'M,L&2L  L&%L&*MMMz>y#PWP]P]_f_m_movo}o}@G@N@NPWP_P_Pb c.*7$$Y@)$$Y@)%%j'//B*%%j'//B*--.@'//R--.@'//R&&{GOOD+&&{GOOD+y)ZUgi{~EGRT_`, lI}g z>:x 9g{I~ &   T|6s"& ('""$ > fV]_ G M Mg V' .W AGPP14P6?P;!QuantizedConv2DWithBiasSumAndRelu+TV_QuantizedConv2DWithBiasSumAndRelu_Tinput,TV_QuantizedConv2DWithBiasSumAndRelu_Tfilter-TV_QuantizedConv2DWithBiasSumAndRelu_out_typecBtjxstj}|j}|jrA t j |d| ||||||||d| d|d| d| d| }t j|}|St|t t"fst%d |z|Dcgc]}t'j(|d}}t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | | | | \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t||||||||| || | | | |S#tj$rYwxYwcc}wcc}wcc}w)aWTODO: add doc. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor` of type `float32`. min_input: A `Tensor` of type `float32`. max_input: A `Tensor` of type `float32`. min_filter: A `Tensor` of type `float32`. max_filter: A `Tensor` of type `float32`. summand: A `Tensor` of type `float32`. strides: A list of `ints`. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rr\rfr`rrNr]Expected list for 'strides' argument to 'quantized_conv2d_with_bias_sum_and_relu' Op, not %r.r_Expected list for 'dilations' argument to 'quantized_conv2d_with_bias_sum_and_relu' Op, not %r.bExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias_sum_and_relu' Op, not %r.)rrrr;r<r{r|r rfr`r\rrr r^r) r&rr'r(rr)(_QuantizedConv2DWithBiasSumAndReluOutputr+r,r-r.r/r06quantized_conv2d_with_bias_sum_and_relu_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrG)rrrr;r<r{r|r rfr`r\rrr rHrIrJrKrorLrMrNrOrPs rQ'quantized_conv2d_with_bias_sum_and_relur%S%s_4    0h..0$ #\\  11 149j*gz)Wi+>< 1g 9>>wGg n GdE] +  @BI J KK9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  @BK L MM=FFbx  [1F)FL L4- 0  @BN O PPCOOB(##B7O,O'88+526)7@8B8B57@:F26 8!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L +\67L 4 : :7 C' .w  & &- ##At,,  # #    C y)Z HgwLtOO  # #    AGPrz)raw_ops.QuantizedConv2DWithBiasSumAndReluct|ttfstd|z|Dcgc]}t j |d}}t j | d} | tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||||g}d |d |d| d|d| d| d | f}t j$d d |||| }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nr rfr`r\rr!rr"rr^rs!QuantizedConv2DWithBiasSumAndRelurrTr)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGr#r+)rrrr;r<r{r|r rfr`r\rrr r!rorcrrPrOrJs rQr$r$%s7 GdE] +  @BI J KK9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  @BK L MM=FFbx  [1F)FL L4- 0  @BN O PPCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | || | ||||S#tj$rYwxYwcc}wcc}wcc}w)r r'r\rfr`rrNrlExpected list for 'strides' argument to 'quantized_conv2d_with_bias_sum_and_relu_and_requantize' Op, not %r.rnExpected list for 'dilations' argument to 'quantized_conv2d_with_bias_sum_and_relu_and_requantize' Op, not %r.qExpected list for 'padding_list' argument to 'quantized_conv2d_with_bias_sum_and_relu_and_requantize' Op, not %r.rrrr;r<r{r|rrr r r r r^rrr)r') r&rr'r(rr)5_QuantizedConv2DWithBiasSumAndReluAndRequantizeOutputr+r,r-r.r/r0Equantized_conv2d_with_bias_sum_and_relu_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQ6quantized_conv2d_with_bias_sum_and_relu_and_requantizer3%sS<    0h..0$ #\\  11 >eiJ .Z9gyi Gg FKKGTg n GdE] +  OQX Y ZZ9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  OQZ [ \\=FFbx  [1F)FL L4- 0  OQ] ^ __COOB(##B7O,O'888E@EEAGE@DEEN E EN E FP EFPEN`EN`ECJEGREGRECJECJEDLE EN!E"HT#E$@D%E!QX& QK' ""$**84i  +W  ):  ,j  ,ill9%y#,,y2I3<< 4nll>*,F::L 8,PWY A G G P' .K  & &- ##At,,  # #    R y)Z 0'; '7Lt OO  # #    AGPrz6raw_ops.QuantizedConv2DWithBiasSumAndReluAndRequantizect| ttfstd| z| Dcgc]}t j |d} }t j | d} |tj}t j|d}|gd}t|ttfstd|z|Dcgc]}t j |d}}|g}t|ttfstd|z|Dcgc]}t j |d }}t j|g|tjtjtjtjtjg\}\}t j|g|tjtjtjtjtjg\}\}t j|g|tjtjg\}\}t j| g|tjtjtjtjtjg\}\} t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"|tj}t!j"| tj} t!j"| tj} |||||||||| | | g }d |d |d |d |d|d| d| d|d |f}t j$dd||||}t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)Nr.rfr`r\rr/rr0rr^rrrs.QuantizedConv2DWithBiasSumAndReluAndRequantizerrTr')rkrlrmr5r;r<rnrWrr{rVrrrrrZr.rrrArGr1r+rs rQr2r2d&s# GdE] +  OQX Y ZZ9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  OQZ [ \\=FFbx  [1F)FL L4- 0  OQ] ^ __COOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<>y#PWP]P]_f_m_movo}o}@G@N@NPWP_P_Pb c.*7$$Y@)$$Y@)%%j'//B*%%j'//B*--.@'//R--.@'//R&&{GOOD+&&{GOOD+y)ZUgi{~EGRT_`, lI}g z>:x 9g{I~ &   N|6s"& ('""$ 8,PWY A G G P' .W AGPrQuantizedDepthwiseConv2D"TV_QuantizedDepthwiseConv2D_Tinput#TV_QuantizedDepthwiseConv2D_Tfilter$TV_QuantizedDepthwiseConv2D_out_typec ltjxstj} | j} | jr= t j | d| ||||||d|d|d|d| } t j| } | St|t t"fst%d|z|Dcgc]}t'j(|d}}t'j*|d}|t,j.}t'j0|d}| gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | \}}}}|dd} t'j6rd |j9d d |j9d d|j9dd|j;dd|j;dd|j;df }|j<}t'j>d||| t j| } | S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | |  S#tj$rY,wxYwcc}wcc}w)aComputes quantized depthwise Conv2D. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original input tensor. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original filter tensor. min_input: A `Tensor` of type `float32`. The float value that the minimum quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the maximum quantized input value represents. min_filter: A `Tensor` of type `float32`. The float value that the minimum quantized filter value represents. max_filter: A `Tensor` of type `float32`. The float value that the maximum quantized filter value represents. strides: A list of `ints`. List of stride values. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. The type of the output. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. List of dilation values. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. r5r\rfr`rNr~PExpected list for 'strides' argument to 'quantized_depthwise_conv2d' Op, not %r.rRExpected list for 'dilations' argument to 'quantized_depthwise_conv2d' Op, not %r.rr^r) r&rr'r(rr)_QuantizedDepthwiseConv2DOutputr+r,r-r.r/r0)quantized_depthwise_conv2d_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQquantized_depthwise_conv2dr>&s>    0h..0$ #\\  11 ($vy:z:xG[)=g055g>g n GdE] +  35< = >>9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  35> ? @@=FFbx  [1F)F'88"%.79/9/97,3h.7d D!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4 6F ::L "L&'C + 1 1' :' .]  & &- ##At,,  # #    6 Iz:WgDd44  # #    AGrz raw_ops.QuantizedDepthwiseConv2Dc bt|ttfstd|z|D cgc]} t j | d}} t j |d}|tj}t j|d}| gd} t| ttfstd| z| D cgc]} t j | d} } t j|g| tjtjtjtjtjg\} \}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}||||||g}d| d |d|d|d|d| f }t j$d d ||| | }t j&rt j(d |||t*j-|}|Scc} wcc} w)Nr:rfr`r\rr;rr^rsQuantizedDepthwiseConv2DrrTr5)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGr<r+rs rQr=r=&s GdE] +  35< = >>9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  35> ? @@=FFbx  [1F)F#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%< t j | d| |||||||d| d|d|d| }t j|}|St|t t"fst%d|z|Dcgc]}t'j(|d}}t'j*|d}| t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4d|||||||||| | | \}}}}|dd}t'j6rd |j9d d |j9d d|j9dd|j;dd|j;dd|j;df }|j<}t'j>d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||| ||| | |  S#tj$rY.wxYwcc}wcc}w)aComputes quantized depthwise Conv2D with Bias. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original input tensor. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original filter tensor. bias: A `Tensor` of type `float32`. The original bias tensor. min_input: A `Tensor` of type `float32`. The float value that the minimum quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the maximum quantized input value represents. min_filter: A `Tensor` of type `float32`. The float value that the minimum quantized filter value represents. max_filter: A `Tensor` of type `float32`. The float value that the maximum quantized filter value represents. strides: A list of `ints`. List of stride values. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. The type of the output. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. List of dilation values. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. r@r\rfr`rNr~ZExpected list for 'strides' argument to 'quantized_depthwise_conv2d_with_bias' Op, not %r.r\Expected list for 'dilations' argument to 'quantized_depthwise_conv2d_with_bias' Op, not %r.) rrrr;r<r{r|rfr`r\rr r^r) r&rr'r(rr)'_QuantizedDepthwiseConv2DWithBiasOutputr+r,r-r.r/r03quantized_depthwise_conv2d_with_bias_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrG)rrrr;r<r{r|rfr`r\rr rHrIrJrKrorLrMrNrOrPs rQ$quantized_depthwise_conv2d_with_biasrI''s@    0h..0$ #\\  11 0$vt9j*j(7Iw YHg8==gFg n GdE] +  =?F G HH9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  =?H I JJ=FFbx  [1F)F'88*%156?7A7A4;W5=6?dL!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4 6F ::L *L&'K 3 9 9' B' .a  & &- ##At,,  # #    @ y)ZWgDd44  # #    AGs<d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||| ||| | | | S#tj$rYwxYwcc}wcc}wcc}w)a,Computes quantized depthwise Conv2D with Bias and Relu. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original input tensor. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original filter tensor. bias: A `Tensor` of type `float32`. The original bias tensor. min_input: A `Tensor` of type `float32`. The float value that the minimum quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the maximum quantized input value represents. min_filter: A `Tensor` of type `float32`. The float value that the minimum quantized filter value represents. max_filter: A `Tensor` of type `float32`. The float value that the maximum quantized filter value represents. strides: A list of `ints`. List of stride values. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. The type of the output. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. List of dilation values. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rKr\rfr`rrNrcExpected list for 'strides' argument to 'quantized_depthwise_conv2d_with_bias_and_relu' Op, not %r.reExpected list for 'dilations' argument to 'quantized_depthwise_conv2d_with_bias_and_relu' Op, not %r.hExpected list for 'padding_list' argument to 'quantized_depthwise_conv2d_with_bias_and_relu' Op, not %r.rr^r) r&rr'r(rr)._QuantizedDepthwiseConv2DWithBiasAndReluOutputr+r,r-r.r/r0< 1g ?DDWMg n GdE] +  FHO P QQ9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  FHQ R SS=FFbx  [1F)FL L4- 0  FHT U VVCOOB(##B7O,O'881v8<=F=F>H>H;B;B!QX QK' ""$**84i  +Z  ,ill9%y#,,y2I3<< 4nll>* ,F ::L 1<R : @ @ I' .y  & &- ##At,,  # #    I y)ZWgLtOO  # #    AGPrz/raw_ops.QuantizedDepthwiseConv2DWithBiasAndRelucPt|ttfstd|z|Dcgc]}t j |d}}t j |d}| tj} t j| d} | gd} t| ttfstd| z| Dcgc]}t j |d} }| g} t| ttfstd| z| Dcgc]}t j |d } }t j|g| tjtjtjtjtjg\}\}t j|g| tjtjtjtjtjg\}\}tj |tj"}tj |tj"}tj |tj"}tj |tj"}tj |tj"}|||||||g}d |d |d| d|d|d| d | f}t j$d d ||| | }t j&rt j(d|||t*j-|}|Scc}wcc}wcc}w)NrPrfr`r\rrQrrRrr^rs'QuantizedDepthwiseConv2DWithBiasAndRelurrTrK)rkrlrmr5r;r<rnrWrr{rVrrrrr.rrZrrArGrSr+rs rQrTrT (s GdE] +  FHO P QQ9@ @"X  r9 - @' @   gy 1' ~~H   * 5(I Ie} -  FHQ R SS=FFbx  [1F)FL L4- 0  FHT U VVCOOB(##B7O,O#::E7C'--Y`YgYgipiwiwzAzHzHJQJYJYJ\],%<TV_QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize_Tinput?TV_QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize_Tfilter=TV_QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize_Tbias@TV_QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize_out_typechtjxstj}|j}|jrB t j |d||||||||||d| d| d| d| d| }t j|}|St| t t"fst%d | z| Dcgc]}t'j(|d} }t'j*| d} | t,j.} t'j0| d} | gd } t| t t"fst%d | z| Dcgc]}t'j(|d} }| g} t| t t"fst%d | z| Dcgc]}t'j(|d} }t3j4 d|||||||||| | | | | |d \}}}}|dd}t'j6rd|j9dd|j9dd|j9dd|j9dd|j;dd|j;dd|j;dd|j;df}|j<}t'j>d|||t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | ||S#tj$rYwxYwcc}wcc}wcc}w)a9Computes quantized depthwise Conv2D with Bias, Relu and Requantize. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original input tensor. filter: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The original filter tensor. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. The original bias tensor. min_input: A `Tensor` of type `float32`. The float value that the minimum quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the maximum quantized input value represents. min_filter: A `Tensor` of type `float32`. The float value that the minimum quantized filter value represents. max_filter: A `Tensor` of type `float32`. The float value that the maximum quantized filter value represents. min_freezed_output: A `Tensor` of type `float32`. The minimum float value of the output tensor. max_freezed_output: A `Tensor` of type `float32`. The maximum float value of the output tensor. strides: A list of `ints`. List of stride values. padding: A `string` from: `"SAME", "VALID"`. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. The type of the output. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. List of dilation values. padding_list: An optional list of `ints`. Defaults to `[]`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor` of type `out_type`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rWr\rfr`rrNrrExpected list for 'strides' argument to 'quantized_depthwise_conv2d_with_bias_and_relu_and_requantize' Op, not %r.rtExpected list for 'dilations' argument to 'quantized_depthwise_conv2d_with_bias_and_relu_and_requantize' Op, not %r.wExpected list for 'padding_list' argument to 'quantized_depthwise_conv2d_with_bias_and_relu_and_requantize' Op, not %r.rr^rr)rW) r&rr'r(rr);_QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutputr+r,r-r.r/r0Kquantized_depthwise_conv2d_with_bias_and_relu_and_requantize_eager_fallbackr4rkrlrmr5r;r<rnrWrr{r?r@rArErCrFrGrs rQKFKGMEIJSJSKUKUSeSeHOHOIQJSMYEIK!QX QK' ""$**84i  +W  ):  ,ill9%y#,,y2I3<< 4nll>* ,F::L > fV]_ G M Mg V' .C  & &- ##At,,  # #    X y)Z 087i#$D ::  # #    AGPrz fV]_ G M Mg V' .O AGPrQuantizedMatMulWithBias)outrfrgTV_QuantizedMatMulWithBias_T1TV_QuantizedMatMulWithBias_T2 TV_QuantizedMatMulWithBias_Tbias"TV_QuantizedMatMulWithBias_Toutput MIN_FIRSTabmin_amax_amin_bmax_bToutput transpose_a transpose_binput_quant_modec tjxstj} | j} | jr> t j | d| |||||||d|d|d| d| }t j|}|S|tj }t#j$|d}|d}t#j&|d}| d} t#j&| d} | d } t#j(| d} t+j,d|||||||||| | | \}}}}|dd}t#j.rd |j1d d |j1d d |j1d d|j1dd|j3dd|j3dd|j5df}|j6}t#j8d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | | |  S#tj$rYwxYw)aPerforms a quantized matrix multiplication of `a` by the matrix `b` with bias add. The inputs must be two-dimensional matrices and 1D bias vector. And the inner dimension of `a` (after being transposed if `transpose_a` is non-zero) must match the outer dimension of `b` (after being transposed if `transposed_b` is non-zero). Then do broadcast add operation with bias values on the matrix multiplication result. The bias size must match inner dimension of `b`. Args: a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. A 1D bias tensor with size matching inner dimension of `b` (after being transposed if `transposed_b` is non-zero). min_a: A `Tensor` of type `float32`. The float value that the lowest quantized `a` value represents. max_a: A `Tensor` of type `float32`. The float value that the highest quantized `a` value represents. min_b: A `Tensor` of type `float32`. The float value that the lowest quantized `b` value represents. max_b: A `Tensor` of type `float32`. The float value that the highest quantized `b` value represents. Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. transpose_a: An optional `bool`. Defaults to `False`. If true, `a` is transposed before multiplication. transpose_b: An optional `bool`. Defaults to `False`. If true, `b` is transposed before multiplication. input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`. Input data quantization mode. Either MIN_FIRST(default) or SCALED. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (out, min_out, max_out). out: A `Tensor` of type `Toutput`. min_out: A `Tensor` of type `float32`. max_out: A `Tensor` of type `float32`. rdrqrrrsrtNrqrrrsrtr r!Frj rkrlrrmrnrorprqrrrsrtr rorpr)r&rr'r(rr)_QuantizedMatMulWithBiasOutputr+r,r-r.r/r0*quantized_mat_mul_with_bias_eager_fallbackr4rWrr;r{r>rnr?r@rArErDrCrFrGrkrlrrmrnrorprqrrrsrtr rHrIrJrKrLrMrNrOrPs rQquantized_mat_mul_with_biasr{)sT    0h..0$ #\\  11 'q!T5% ui-{$68HJg/44W=g n _nnG   w 2'K""; >+K""; >+"&&'79KL'88!Q!$e).e5+2 /:4D(, .!QX QK' ""$C&&t,dC4F4Ft4Ls))'2I  +]  /  /1Cll-. 0F ::L !<B * 0 0 9' .W  & &- ##At,,  # #    7 QeUE5'!{+$DBB  # #   s0rnrVrrrrrZr.rrrArGrxr+)rkrlrrmrnrorprqrrrsrtr r!rurvrrPrOrJs rQryryj)sE _nnG   w 2'K""; >+K""; >+"&&'79KL22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ!88$wX_XfXfFij+w   8%   8%   8%   8%QeUE59, (D(G[) =+}k& (&   7$0C"& ('""$ !<B * 0 0 9' .r\*TV_QuantizedMatMulWithBiasAndDequantize_T1*TV_QuantizedMatMulWithBiasAndDequantize_T2-TV_QuantizedMatMulWithBiasAndDequantize_Tbias/TV_QuantizedMatMulWithBiasAndDequantize_Toutputcdtjxstj}|j}|jr+ t j |d| |||||||||d| d| d| d| }|Stj| d} | d} tj| d} | d} tj| d} | d } tj | d} t#j$d|||||||||| | | | | \}}}}|dd}tj&rd |j)d d |j)d d |j)d d|j)dd|j+dd|j+dd|j-df}|j.}tj0d||||\}|S#t j$r }tj|| Yd}~nd}~wt j$rYnwxYw t|||||||||| | | | | |S#t j$rYwxYw)aTODO: add doc. Args: a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. min_a: A `Tensor` of type `float32`. max_a: A `Tensor` of type `float32`. min_b: A `Tensor` of type `float32`. max_b: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. Toutput: A `tf.DType` from: `tf.float32`. transpose_a: An optional `bool`. Defaults to `False`. transpose_b: An optional `bool`. Defaults to `False`. input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`. name: A name for the operation (optional). Returns: A `Tensor` of type `Toutput`. $QuantizedMatMulWithBiasAndDequantizerqrrrsrtNrvFrjrkrlrrmrnrorprrrqrrrsrtr rorpr)r&rr'r(rr)r,r-r.r/r09quantized_mat_mul_with_bias_and_dequantize_eager_fallbackr4r;r{r>rnr?r@rArErDrCrFrGrkrlrrmrnrorprrrqrrrsrtr rHrIrJrKrLrMrNrOrPs rQ*quantized_mat_mul_with_bias_and_dequantizer)su,    0h..0$ #\\  11 4dAq$ ue/1C7M; ')9 ;g n   w 2'K""; >+K""; >+"&&'79KL'88.!qt6;56;5CUCU8?HH/.H/z,raw_ops.QuantizedMatMulWithBiasAndDequantizec`tj| d} | d} tj| d} | d} tj| d} | d} tj| d} tj|g|t j t jt jt jt jg\}\}tj|g|t j t jt jt jt jg\}\}tj|g|t jt jg\}\}tj|t j}tj|t j}tj|t j}tj|t j}tj|t j}tj|t j}|||||||||g }d|d|d |d| d| d| d| f}tjd d |||| }tjrtj d ||||\}|S)NrqFrrrsrjrtrorprs$QuantizedMatMulWithBiasAndDequantizersrTr)r;r{r>rnrVrWrrrrrrZr.rrrArGrkrlrrmrnrorprrrqrrrsrtr r!rurvrrPrOrJs rQrr)sd   w 2'K""; >+K""; >+"&&'79KL22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ!88$wX_XfXfFij+w   8%   8%   8%   8%--.@'//R--.@'//RQeUE5:LN`a, (D(G[) =+}k& (&   Da$0C"& ('""$ . fgO (' .r\QuantizedMatMulWithBiasAndRelu$TV_QuantizedMatMulWithBiasAndRelu_T1$TV_QuantizedMatMulWithBiasAndRelu_T2)TV_QuantizedMatMulWithBiasAndRelu_Toutputc tjxstj} | j} | jr> t j | d| |||||||d|d|d| d| }t j|}|S|tj }t#j$|d}|d}t#j&|d}| d} t#j&| d} | d } t#j(| d} t+j,d|||||||||| | | \}}}}|dd}t#j.rd |j1d d |j1d d|j1dd|j3dd|j3dd|j5df }|j6}t#j8d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | | |  S#tj$rYwxYw) a Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion. The inputs must be two-dimensional matrices and 1D bias vector. And the inner dimension of `a` (after being transposed if `transpose_a` is non-zero) must match the outer dimension of `b` (after being transposed if `transposed_b` is non-zero). Then do broadcast add operation with bias values on the matrix multiplication result. The bias size must match inner dimension of `b`. Then do relu activation to get non-negative result. Args: a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. bias: A `Tensor` of type `float32`. A 1D bias tensor with size matching with inner dimension of `b` (after being transposed if `transposed_b` is non-zero). min_a: A `Tensor` of type `float32`. The float value that the lowest quantized `a` value represents. max_a: A `Tensor` of type `float32`. The float value that the highest quantized `a` value represents. min_b: A `Tensor` of type `float32`. The float value that the lowest quantized `b` value represents. max_b: A `Tensor` of type `float32`. The float value that the highest quantized `b` value represents. Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`. transpose_a: An optional `bool`. Defaults to `False`. If true, `a` is transposed before multiplication. transpose_b: An optional `bool`. Defaults to `False`. If true, `b` is transposed before multiplication. input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`. Input data quantization mode. Either MIN_FIRST(default) or SCALED. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (out, min_out, max_out). out: A `Tensor` of type `Toutput`. min_out: A `Tensor` of type `float32`. max_out: A `Tensor` of type `float32`. rrqrrrsrtNrvFrjrwrorp)r&rr'r(rr)%_QuantizedMatMulWithBiasAndReluOutputr+r,r-r.r/r03quantized_mat_mul_with_bias_and_relu_eager_fallbackr4rWrr;r{r>rnr?r@rArErDrCrFrGrzs rQ$quantized_mat_mul_with_bias_and_relur*s{V    0h..0$ #\\  11 .aD% ueY {$68HJg6;;GDg n _nnG   w 2'K""; >+K""; >+"&&'79KL'88(AU05U05w6A6A;K/35!QX QK' ""$C&&t,dC4F4Ft4L++I6   /  /1Cll-. 0F ::L (,I 1 7 7 @' .W  & &- ##At,,  # #    @ QeUE5'!{+$DBB  # #   s0rnrVrrrrr.rrZrrArGrr+)rkrlrrmrnrorprqrrrsrtr r!rurvrPrOrJs rQrrs*s* _nnG   w 2'K""; >+K""; >+"&&'79KL22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ  goo 6$   8%   8%   8%   8%QeUE59, (D(Iw  }k+= &   >$0C"& ('""$ (,I 1 7 7 @' .r\+QuantizedMatMulWithBiasAndReluAndRequantize1TV_QuantizedMatMulWithBiasAndReluAndRequantize_T11TV_QuantizedMatMulWithBiasAndReluAndRequantize_T24TV_QuantizedMatMulWithBiasAndReluAndRequantize_Tbias6TV_QuantizedMatMulWithBiasAndReluAndRequantize_Toutputctjxstj}|j}|jr@ t j |d| |||||||||d| d| d| d| }t j|}|S| tj } t#j$| d} | d} t#j&| d} | d} t#j&| d} | d } t#j(| d} t+j,d|||||||||| | | | | \}}}}|dd}t#j.rd |j1d d |j1d d |j1d d|j1dd|j3dd|j3dd|j5df}|j6}t#j8d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | |S#tj$rYwxYw)a< Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu and requantize fusion. The inputs must be two-dimensional matrices and 1D bias vector. And the inner dimension of `a` (after being transposed if `transpose_a` is non-zero) must match the outer dimension of `b` (after being transposed if `transposed_b` is non-zero). Then do broadcast add operation with bias values on the matrix multiplication result. The bias size must match inner dimension of `b`. Then do relu activation to get non-negative result. Then do requantize operation to get final uint8 result. Args: a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied. Must be a two-dimensional tensor of type `quint8`. b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. A matrix to be multiplied and must be a two-dimensional tensor of type `qint8`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. A 1D bias tensor with size matching with inner dimension of `b` (after being transposed if `transposed_b` is non-zero). min_a: A `Tensor` of type `float32`. The float value that the lowest quantized `a` value represents. max_a: A `Tensor` of type `float32`. The float value that the highest quantized `a` value represents. min_b: A `Tensor` of type `float32`. The float value that the lowest quantized `b` value represents. max_b: A `Tensor` of type `float32`. The float value that the highest quantized `b` value represents. min_freezed_output: A `Tensor` of type `float32`. The float value that the highest quantized output value after requantize. max_freezed_output: A `Tensor` of type `float32`. Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. transpose_a: An optional `bool`. Defaults to `False`. If true, `a` is transposed before multiplication. transpose_b: An optional `bool`. Defaults to `False`. If true, `b` is transposed before multiplication. input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`. Input data quantization mode. Either MIN_FIRST(default) or SCALED. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (out, min_out, max_out). out: A `Tensor` of type `Toutput`. min_out: A `Tensor` of type `float32`. max_out: A `Tensor` of type `float32`. rrqrrrsrtNrvFrjrrorpr)r&rr'r(rr)2_QuantizedMatMulWithBiasAndReluAndRequantizeOutputr+r,r-r.r/r0Bquantized_mat_mul_with_bias_and_relu_and_requantize_eager_fallbackr4rWrr;r{r>rnr?r@rArErDrCrFrGrs rQ3quantized_mat_mul_with_bias_and_relu_and_requantizer*s^    0h..0$ #\\  11 ;T1a ueU$68J7M; ')9 ;g CHHQg n _nnG   w 2'K""; >+K""; >+"&&'79KL'885ad=B=B=B=BJ\J\?FCNCNHX<@ B!QX QK' ""$C&&t,dC4F4Ft4Ls))'2I  +]  /  /1Cll-. 0F ::L 5|VWV > D DW M' .e  & &- ##At,,  # #    O QeUE52D g;!4D   # #   0>G++H2>HH21H26II'&I'z3raw_ops.QuantizedMatMulWithBiasAndReluAndRequantizec| tj} tj| d} | d} tj| d} | d} tj| d} | d} tj | d} tj |g|tjtjtjtjtjg\}\}tj |g|tjtjtjtjtjg\}\}tj |g|tjtjg\}\}tj|tj}tj|tj}tj|tj}tj|tj}tj|tj}tj|tj}|||||||||g }d|d|d |d| d| d| d| f}tjd d |||| }tjrtj d |||t"j%|}|S)NrqFrrrsrjrtrorprs+QuantizedMatMulWithBiasAndReluAndRequantizerrTr)rWrr;r{r>rnrVrrrrrZr.rrrArGrr+rs rQrr+s{ _nnG   w 2'K""; >+K""; >+"&&'79KL22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ!88$wX_XfXfFij+w   8%   8%   8%   8%--.@'//R--.@'//RQeUE5:LN`a, (D(G[) =+}k& (&   K|6s"& ('""$ 5|VWV > D DW M' .r\$QuantizedMatMulWithBiasAndRequantize*TV_QuantizedMatMulWithBiasAndRequantize_T1*TV_QuantizedMatMulWithBiasAndRequantize_T2-TV_QuantizedMatMulWithBiasAndRequantize_Tbias/TV_QuantizedMatMulWithBiasAndRequantize_Toutputctjxstj}|j}|jr@ t j |d| |||||||||d| d| d| d| }t j|}|S| tj } t#j$| d} | d} t#j&| d} | d} t#j&| d} | d } t#j(| d} t+j,d|||||||||| | | | | \}}}}|dd}t#j.rd |j1d d |j1d d |j1d d|j1dd|j3dd|j3dd|j5df}|j6}t#j8d|||t j|}|S#tj$r }tj|| Yd}~nd}~wtj$rYnwxYw t|||||||||| | | | | |S#tj$rYwxYw)aTODO: add doc. Args: a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. bias: A `Tensor`. Must be one of the following types: `float32`, `qint32`. min_a: A `Tensor` of type `float32`. max_a: A `Tensor` of type `float32`. min_b: A `Tensor` of type `float32`. max_b: A `Tensor` of type `float32`. min_freezed_output: A `Tensor` of type `float32`. max_freezed_output: A `Tensor` of type `float32`. Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. transpose_a: An optional `bool`. Defaults to `False`. transpose_b: An optional `bool`. Defaults to `False`. input_quant_mode: An optional `string` from: `"MIN_FIRST", "SCALED"`. Defaults to `"MIN_FIRST"`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (out, min_out, max_out). out: A `Tensor` of type `Toutput`. min_out: A `Tensor` of type `float32`. max_out: A `Tensor` of type `float32`. rrqrrrsrtNrvFrjrrorpr)r&rr'r(rr)+_QuantizedMatMulWithBiasAndRequantizeOutputr+r,r-r.r/r09quantized_mat_mul_with_bias_and_requantize_eager_fallbackr4rWrr;r{r>rnr?r@rArErDrCrFrGrs rQ*quantized_mat_mul_with_bias_and_requantizer<+s4    0h..0$ #\\  11 4dAq$ ue/1C7M; ')9 ;g <AA'Jg n _nnG   w 2'K""; >+K""; >+"&&'79KL'88.!qt6;56;5CUCU8?rnrVrrrrrZr.rrrArGrr+rs rQrr+s{ _nnG   w 2'K""; >+K""; >+"&&'79KL22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ22A3gmmW^^]d]k]kmtm{m{~E~M~M>PQ.(DQ!88$wX_XfXfFij+w   8%   8%   8%   8%--.@'//R--.@'//RQeUE5:LN`a, (D(G[) =+}k& (&   Da$0C"& ('""$ . fgO 7 = =g F' .r\QuantizedMaxPoolTV_QuantizedMaxPool_Tctjxstj}|j}|jr8 t j |d||||d|d|d| } t j| } | St|t t"fst%d|z|D cgc]} t'j(| d}} t|t t"fst%d|z|D cgc]} t'j(| d}} t'j*|d}t-j.d||||||| \} } } }|dd} t'j0rjd | j3d d| j5dd| j5dd| j5df}| j6}t'j8d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||S#tj$rYwxYwcc} wcc} w) aProduces the max pool of the input tensor for quantized types. Args: input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. The 4D (batch x rows x cols x depth) Tensor to MaxReduce over. min_input: A `Tensor` of type `float32`. The float value that the lowest quantized input value represents. max_input: A `Tensor` of type `float32`. The float value that the highest quantized input value represents. ksize: A list of `ints`. The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. strides: A list of `ints`. The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, min_output, max_output). output: A `Tensor`. Has the same type as `input`. min_output: A `Tensor` of type `float32`. max_output: A `Tensor` of type `float32`. rrerfr`Nr>FExpected list for 'ksize' argument to 'quantized_max_pool' Op, not %r.HExpected list for 'strides' argument to 'quantized_max_pool' Op, not %r.rAr%)r&rr'r(rr)_QuantizedMaxPoolOutputr+r,r-r.r/r0!quantized_max_pool_eager_fallbackr4rkrlrmr5r;r<rnr?r@rArErCrFrGrDs rQquantized_max_poolr+rFrGzraw_ops.QuantizedMaxPoolc t|ttfstd|z|Dcgc]}t j |d}}t|ttfstd|z|Dcgc]}t j |d}}t j |d}t j|g|tjtjtjtjtjg\} \}tj|tj }tj|tj }|||g} d| d|d|d|f} t j"dd| | || } t j$rt j&d | | | t(j+| } | Scc}wcc}w) Nrrerrfr`r%sQuantizedMaxPoolrrTr)rkrlrmr5r;r<rnrVrWrrrrrr.rrZrrArGrr+rIs rQrr ,rJrK QuantizedRelu) activationsmin_activationsmax_activationsTV_QuantizedRelu_TinputTV_QuantizedRelu_out_type min_features max_featuresc Rtjxstj}|j}|jr4 t j |d||||d|}t j|}|S|tj }t#j$|d}t'j(d|||||\} } } } | dd}t#j*rHd| j-dd| j-df} | j.} t#j0d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||||S#tj$rY2wxYw)a8Computes Quantized Rectified Linear: `max(features, 0)` Args: features: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_features: A `Tensor` of type `float32`. The float value that the lowest quantized value represents. max_features: A `Tensor` of type `float32`. The float value that the highest quantized value represents. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (activations, min_activations, max_activations). activations: A `Tensor` of type `out_type`. min_activations: A `Tensor` of type `float32`. max_activations: A `Tensor` of type `float32`. rr\Nrnrrrr\r r^)r&rr'r(rr)_QuantizedReluOutputr+r,r-r.r/r0quantized_relu_eager_fallbackr4rWrr;r{r?r@rArErFrGrrrr\r rHrIrJrKrLrMrNrOrPs rQquantized_relur.,s&    0h..0$ #\\  11 otX|\Hg%**73g n~~H   * 5('88(&2X"$!QX QK' ""$**84j  ,.F::L vw8 & &w /' .5  & &- ##At,,  # #    * L,  # #   02D33E:E!!E:9E:>FF&%F&zraw_ops.QuantizedReluc |tj}tj|d}tj|g|tj tjtj tjtjg\}\}tj|tj}tj|tj}|||g}d|d|f}tjdd||||} tjrtjd||| tj!| } | S)Nr\r^s QuantizedRelurrTr)rWrr;r{rVrrrrr.rrZrrArGrr+ rrrr\r r!rcrPrOrJs rQrri,s) ~~H   * 5(&==xj#PWP]P]_f_m_movo}o}@G@N@NPWP_P_Pbc, '' gooF,'' gooF,L,7, lJ 9&   -q#)s ?' ""$ vw8 & &w /' .r\QuantizedRelu6TV_QuantizedRelu6_TinputTV_QuantizedRelu6_out_typec Rtjxstj}|j}|jr4 t j |d||||d|}t j|}|S|tj }t#j$|d}t'j(d|||||\} } } } | dd}t#j*rHd| j-dd| j-df} | j.} t#j0d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||||S#tj$rY2wxYw)aBComputes Quantized Rectified Linear 6: `min(max(features, 0), 6)` Args: features: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. min_features: A `Tensor` of type `float32`. The float value that the lowest quantized value represents. max_features: A `Tensor` of type `float32`. The float value that the highest quantized value represents. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (activations, min_activations, max_activations). activations: A `Tensor` of type `out_type`. min_activations: A `Tensor` of type `float32`. max_activations: A `Tensor` of type `float32`. rr\Nrnrr^)r&rr'r(rr)_QuantizedRelu6Outputr+r,r-r.r/r0quantized_relu6_eager_fallbackr4rWrr;r{r?r@rArErFrGrs rQquantized_relu6r,s&    0h..0$ #\\  11 h lHg&++G4g n~~H   * 5('888,'3h#%!QX QK' ""$**84j  ,.F::L ,9 ! ' ' 0' .5  & &- ##At,,  # #    + L,  # #   rzraw_ops.QuantizedRelu6c |tj}tj|d}tj|g|tj tjtj tjtjg\}\}tj|tj}tj|tj}|||g}d|d|f}tjdd||||} tjrtjd||| tj!| } | S)Nr\r^sQuantizedRelu6rrTr)rWrr;r{rVrrrrr.rrZrrArGrr+rs rQrr,s) ~~H   * 5(&==xj#PWP]P]_f_m_movo}o}@G@N@NPWP_P_Pbc, '' gooF,'' gooF,L,7, lJ 9&   .,#)s ?' ""$ ,9 ! ' ' 0' .r\QuantizedReluXTV_QuantizedReluX_TinputTV_QuantizedReluX_out_type max_valuec Xtjxstj}|j}|jr5 t j |d|||||d| }t j|}|S|tj }t#j$|d}t'j(d||||||\} } } } | dd}t#j*rHd| j-dd| j-df} | j.}t#j0d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rY4wxYw)awComputes Quantized Rectified Linear X: `min(max(features, 0), max_value)` Args: features: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. max_value: A `Tensor` of type `float32`. min_features: A `Tensor` of type `float32`. The float value that the lowest quantized value represents. max_features: A `Tensor` of type `float32`. The float value that the highest quantized value represents. out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (activations, min_activations, max_activations). activations: A `Tensor` of type `out_type`. min_activations: A `Tensor` of type `float32`. max_activations: A `Tensor` of type `float32`. rr\Nrn)rrrrr\r r^)r&rr'r(rr)_QuantizedReluXOutputr+r,r-r.r/r0quantized_relu_x_eager_fallbackr4rWrr;r{r?r@rArErFrG)rrrrr\r rHrIrJrKrLrMrNrOrPs rQquantized_relu_xr,s(    0h..0$ #\\  11 h <j(,g&++G4g n~~H   * 5('888y'3'3h# %!QX QK' ""$**84j  ,.F::L ,9 ! ' ' 0' .7  & &- ##At,,  # #    , I|\H  # #   s03D55E<E##E<;E<FF)(F)zraw_ops.QuantizedReluXc |tj}tj|d}tj|g|tj tjtj tjtjg\}\}tj|tj}tj|tj}tj|tj}||||g}d|d|f} tjdd|| ||} tjrtjd|| | tj!| } | S)Nr\r^sQuantizedReluXrrTr)rWrr;r{rVrrrrr.rrZrrArGrr+) rrrrr\r r!rcrPrOrJs rQrr-sA ~~H   * 5(&==xj#PWP]P]_f_m_movo}o}@G@N@NPWP_P_Pbc, $$Y@)'' gooF,'' gooF,I|\B, lJ 9&   .,#)s ?' ""$ ,9 ! ' ' 0' .r\ TV_Relu_Tznn.reluc >tjxstj}|j}|jr t j |d||}|St||fd}|tur|S t/j0d||\}}}}|dd}t3j4r7d|j7df} |j8} t3j:d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||fd}|tur|St|||S#t j$rYtt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw#tt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw)aComputes rectified linear: `max(features, 0)`. See: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) Example usage: >>> tf.nn.relu([-2., 0., 3.]).numpy() array([0., 0., 3.], dtype=float32) Args: features: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`, `qint8`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. ReluNrr"rr%)r&rr'r(rr)r,r-r.r/r0_dispatcher_for_relur2relu_eager_fallbackr4r5r6r7r relur8r9r:r?r@r;rArErFrGrs rQrr(-s$    0h..0$ #\\ 11 fdH&g n,# 44!Gn$ n  )::.Aq#x QK' ""$3%%c* +F::L  fg/ (' .W  & &- ##At,,  # #    $ T T#g  & 4))  # #  z " "" "dH48 g  ..<< <  Z    D(6 Gi,,::: n  rz raw_ops.Relucjtj|g|tjtjtj tj tjtjtjtjtjtjtjtjtjg \}\}|g}d|f}tj dd||||}tj"rtj$d||||\}|S)Nr%sRelursrTr)r;rVrWrZrurrrrrrYrrXrrrrrArGrs rQrrr-sm!88(S7??\c\k\kmtmzmz}D}J}JLSLYLY[b[g[gipivivxxHxHJQJXJXZaZfZfhohvhvxxFxFHOHUHUKXY';H, >&   Wa F!$4 1' ""$  fg/ (' .r\ TV_Relu6_Tc~tjxstj}|j}|jr t j |d||}|Stjd||\}}}}|dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)abComputes rectified linear 6: `min(max(features, 0), 6)`. Args: features: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. Relu6Nrrr%)r&rr'r(rr)r,r-r.r/r0relu6_eager_fallbackr4r?r@r;rArErFrGrs rQrelu6r-s7    0h..0$ #\\ 11 gtX'g n(88(/!QX QK' ""$3%%c* +F::L vw0 (' .'  & &- ##At,,  # #    ! 4))  # #   rz raw_ops.Relu6cLtj|g|tjtjtj tj tjtjtjtjtjtjtjtjg \}\}|g}d|f}tjdd||||}tj rtj"d||||\}|S)Nr%sRelu6rsrTrr;rVrWrZrurrrrrrYrrXrrrrArGrs rQrr-sX!88(S7??\c\k\kmtmzmz}D}J}JLSLYLY[b[g[gipivivxxHxHJQJXJXZaZfZfhohvhvxxFxFKIJ';H, >&   XqV!$4 1' ""$ vw0 (' .r\TV_Relu6Grad_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)a|Computes rectified linear 6 gradients for a Relu6 operation. Args: gradients: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The backpropagated gradients to the corresponding Relu6 operation. features: A `Tensor`. Must have the same type as `gradients`. The features passed as input to the corresponding Relu6 operation, or its output; using either one produces the same result. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. Relu6GradNrrrr r%)r&rr'r(rr)r,r-r.r/r0relu6_grad_eager_fallbackr4r?r@r;rArErFrG rrr rHrIrJrKrLrMrNrOrPs rQ relu6_gradr-s>    0h..0$ #\\ 11 k4H6g n(88y8$H!QX QK' ""$3%%c* +F::L \674 (' .'  & &- ##At,,  # #    & XDd44  # #   rzraw_ops.Relu6GradcVtj||g|tjtjtj tj tjtjtjtjtjtjtjtjg \}}|\}}||g}d|f}tjdd||||}tj rtj"d||||\}|S)Nr%s Relu6GradrsrTrr rrr r!r[rrPrOrJs rQrr-sq66 87LcT[TcTceletetv}wDwDFMFSFSU\UbUbdkdpdpryrrAHAQAQSZSaSacjcocoqxqqAHAOAOTRS'9#9hX&, >&   \1\#)s ?' ""$ \674 (' .r\ TV_ReluGrad_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)axComputes rectified linear gradients for a Relu operation. Args: gradients: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. The backpropagated gradients to the corresponding Relu operation. features: A `Tensor`. Must have the same type as `gradients`. The features passed as input to the corresponding Relu operation, OR the outputs of that operation (both work equivalently). name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. ReluGradNrrr%)r&rr'r(rr)r,r-r.r/r0relu_grad_eager_fallbackr4r?r@r;rArErFrGrs rQ relu_gradr-s>    0h..0$ #\\ 11 j$ 85g n(88i(G!QX QK' ""$3%%c* +F::L L&'3 (' .'  & &- ##At,,  # #    % XDd44  # #   rzraw_ops.ReluGradcVtj||g|tjtjtj tj tjtjtjtjtjtjtjtjg \}}|\}}||g}d|f}tjdd||||}tj rtj"d||||\}|S)Nr%sReluGradrsrTrrrs rQrr#.sq66 87LcT[TcTceletetv}wDwDFMFSFSU\UbUbdkdpdpryrrAHAQAQSZSaSacjcocoqxqqAHAOAOTRS'9#9hX&, >&   [!L#)s ?' ""$ L&'3 (' .r\ TV_Selu_Tznn.seluc >tjxstj}|j}|jr t j |d||}|St||fd}|tur|S t/j0d||\}}}}|dd}t3j4r7d|j7df} |j8} t3j:d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||fd}|tur|St|||S#t j$rYtt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw#tt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw)aBComputes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise. To be used together with `initializer = tf.variance_scaling_initializer(factor=1.0, mode='FAN_IN')`. For correct dropout, use `tf.contrib.nn.alpha_dropout`. See [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515) Args: features: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. SeluNrr"rr%)r&rr'r(rr)r,r-r.r/r0_dispatcher_for_selur2selu_eager_fallbackr4r5r6r7r selur8r9r:r?r@r;rArErFrGrs rQrr3.s*    0h..0$ #\\ 11 fdH&g n,# 44!Gn$ n  )::.Aq#x QK' ""$3%%c* +F::L  fg/ (' .W  & &- ##At,,  # #    $ T T#g  & 4))  # #  z " "" "dH48 g  ..<< <  Z    D(6 Gi,,::: n  rz raw_ops.Seluc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sSelursrTrrrs rQrr.s!88(S7<&   Wa F!$4 1' ""$  fg/ (' .r\ TV_SeluGrad_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aComputes gradients for the scaled exponential linear (Selu) operation. Args: gradients: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. The backpropagated gradients to the corresponding Selu operation. outputs: A `Tensor`. Must have the same type as `gradients`. The outputs of the corresponding Selu operation. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. SeluGradNrrr%)r&rr'r(rr)r,r-r.r/r0selu_grad_eager_fallbackr4r?r@r;rArErFrGrs rQ selu_gradr.s>    0h..0$ #\\ 11 j$ 74g n(88itE!QX QK' ""$3%%c* +F::L L&'3 (' .'  & &- ##At,,  # #    % W4T33  # #   rzraw_ops.SeluGradcftj||g|tjtjtj tj g\}}|\}}||g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sSeluGradrsrTrrrs rQrr.s66 77KSSZS_S_ahaqaqsztCtCELETETSWX'9"9gW%, >&   [!L#)s ?' ""$ L&'3 (' .r\ TV_Softmax_Tc~tjxstj}|j}|jr t j |d||}|Stjd||\}}}}|dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)aComputes softmax activations. For each batch `i` and class `j` we have $$softmax[i, j] = exp(logits[i, j]) / sum_j(exp(logits[i, j]))$$ Args: logits: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 2-D with shape `[batch_size, num_classes]`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `logits`. SoftmaxNrrr%)r&rr'r(rr)r,r-r.r/r0softmax_eager_fallbackr4r?r@r;rArErFrGrs rQsoftmaxr.s7    0h..0$ #\\ 11 iv'g n(88&t-!QX QK' ""$3%%c* +F::L <2 (' .'  & &- ##At,,  # #    # t''  # #   rzraw_ops.Softmaxc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sSoftmaxrsrTrrrs rQrr.s66xw||U\UeUegngvgvxyHyHGKL'9F, >&   Z&   =q$0C"& ('""$ 'vwH 0 6 6w ?' .r\ TV_Softplus_Tc~tjxstj}|j}|jr t j |d||}|Stjd||\}}}}|dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||S#t j$rYwxYw)zTODO: add doc. Args: features: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. SoftplusNrrr%)r&rr'r(rr)r,r-r.r/r0softplus_eager_fallbackr4r?r@r;rArErFrGrs rQsoftplusrR/s7    0h..0$ #\\ 11 j$*g n(88XD2!QX QK' ""$3%%c* +F::L L&'3 (' .'  & &- ##At,,  # #    $ 4))  # #   rzraw_ops.Softplusc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sSoftplusrsrTrrrs rQrr{/!88(S7<&   [!L#)s ?' ""$ L&'3 (' .r\TV_SoftplusGrad_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aComputes softplus gradients for a softplus operation. Args: gradients: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. The backpropagated gradients to the corresponding softplus operation. features: A `Tensor`. Must have the same type as `gradients`. The features passed as input to the corresponding softplus operation. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. SoftplusGradNrrr%)r&rr'r(rr)r,r-r.r/r0softplus_grad_eager_fallbackr4r?r@r;rArErFrGrs rQ softplus_gradr/>    0h..0$ #\\ 11 ndIx9g n(88)hTK!QX QK' ""$3%%c* +F::L  fg7 (' .'  & &- ##At,,  # #    ) XDd44  # #   rzraw_ops.SoftplusGradcftj||g|tjtjtj tj g\}}|\}}||g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%s SoftplusGradrsrTrrrs rQrr/66 87LcT[T`T`bibrbrt{uDuDFMFUFUTXY'9#9hX&, >&   _a #)s ?' ""$  fg7 (' .r\ TV_Softsign_Tz nn.softsignz math.softsignc >tjxstj}|j}|jr t j |d||}|St||fd}|tur|S t/j0d||\}}}}|dd}t3j4r7d|j7df} |j8} t3j:d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||fd}|tur|St|||S#t j$rYtt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw#tt f$rHt#j$t&dt)||}|t"j*j,ur|cYSwxYw)aComputes softsign: `features / (abs(features) + 1)`. Args: features: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `features`. SoftsignNrr"rr%)r&rr'r(rr)r,r-r.r/r0_dispatcher_for_softsignr2softsign_eager_fallbackr4r5r6r7r softsignr8r9r:r?r@r;rArErFrGrs rQr!r!/s    0h..0$ #\\ 11 j$*g n,' 44!Gn$ n  )::XD2Aq#x QK' ""$3%%c* +F::L L&'3 (' .W  & &- ##At,,  # #    ( T T#g  & $ 4))  # #  z " "" b$t< g  ..<< <  Z    BhT: Gi,,::: n  rzraw_ops.Softsignc\tj|g|tjtjtj tj g\}\}|g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%sSoftsignrsrTrrrs rQr r  0rr\TV_SoftsignGrad_Tctjxstj}|j}|jr t j |d|||}|Stjd|||\}}}} | dd}tj r7d|j#df} |j$} tj&d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||S#t j$rYwxYw)aComputes softsign gradients for a softsign operation. Args: gradients: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. The backpropagated gradients to the corresponding softsign operation. features: A `Tensor`. Must have the same type as `gradients`. The features passed as input to the corresponding softsign operation. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `gradients`. SoftsignGradNrrr%)r&rr'r(rr)r,r-r.r/r0softsign_grad_eager_fallbackr4r?r@r;rArErFrGrs rQ softsign_gradr'0rrzraw_ops.SoftsignGradcftj||g|tjtjtj tj g\}}|\}}||g}d|f}tjdd||||}tjrtjd||||\}|S)Nr%s SoftsignGradrsrTr%rrs rQr&r&F0rr\#SparseSoftmaxCrossEntropyWithLogits(TV_SparseSoftmaxCrossEntropyWithLogits_T.TV_SparseSoftmaxCrossEntropyWithLogits_Tlabelsctjxstj}|j}|jr1 t j |d|||}t j|}|Stj d|||\}}}} | dd}t#j$rHd|j'dd|j'df} |j(} t#j*d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||S#tj$rYwxYw)aComputes softmax cross entropy cost and gradients to backpropagate. Unlike `SoftmaxCrossEntropyWithLogits`, this operation does not accept a matrix of label probabilities, but rather a single label per row of features. This label is considered to have probability 1.0 for the given row. Inputs are the logits, not probabilities. Args: features: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. batch_size x num_classes matrix labels: A `Tensor`. Must be one of the following types: `int32`, `int64`. batch_size vector with values in [0, num_classes). This is the label for the given minibatch entry. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (loss, backprop). loss: A `Tensor`. Has the same type as `features`. backprop: A `Tensor`. Has the same type as `features`. r)Nrrr%Tlabels)r&rr'r(rr)*_SparseSoftmaxCrossEntropyWithLogitsOutputr+r,r-r.r/r07sparse_softmax_cross_entropy_with_logits_eager_fallbackr4r?r@r;rArErFrGr s rQ(sparse_softmax_cross_entropy_with_logitsr0[0so0    0h..0$ #\\ 11 3T8VMg:@@Ig n(88-6<4I!QX QK' ""$3%%c*I  +-F::L -|VWN 6 < = 0`. Number of top elements to look for along the last dimension (along each row for matrices). sorted: An optional `bool`. Defaults to `True`. If true the resulting `k` elements will be sorted by the values in descending order. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (values, indices). values: A `Tensor`. Has the same type as `input`. indices: A `Tensor` of type `int32`. r3rr5N)rr5r r!T)rrr5r r%)r&rr'r(rr) _TopKOutputr+r,r-r.r/r0top_k_eager_fallbackr4r;r<r>r?r@rArBrDrErFrG) rrr5r rHrIrJrKrLrMrNrOrPs rQtop_kr90sB    0h..0$ #\\ 11 fdE38V=g!!'*g n3! ^ F   fh /&'88eqd<!QX QK' ""$3$$S)8  *C1C1CC1HJF::L  fg/   g &' .1  & &- ##At,,  # #    ! 1V$D::  # #   s02E FE99FFF&&F=<F=z raw_ops.TopKctj|d}|d}tj|d}tj|g|tj tj tjtjtjtjtjtjtjtjtjtj g \}\}|g}d|d|d|f}tj"dd||||}tj$rtj&d|||t(j+|}|S) NrTr5r%sTopKrSrTr3)r;r<r>rVrWrZrurrrrrrYrrXrrrrArGr7r+) rrr5r r!r[rPrOrJs rQr8r80s3! ^ F   fh /&55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjcpcpryrBrBDKDRDRT[T`T`bibpbpryr@r@ECD'8E, Hfc7 3&   Wa F!$4 1' ""$  fg/   g &' .r\TopKV2 TV_TopKV2_T TV_TopKV2_TkTV_TopKV2_index_type index_typec tjxstj}|j}|jr5 t j |d|||d|d| }t j|}|S|d}tj |d}|t"j$}tj&|d}t)j*d|||||\} } } } | dd}tj,rjd| j/dd| j1dd | j1d d| j1df} | j2} tj4d| | |t j|}|S#tj$r }tj||Yd}~nd}~wtj$rYnwxYw t||||||S#tj$rYnwxYw) aFinds values and indices of the `k` largest elements for the last dimension. If the input is a vector (rank-1), finds the `k` largest entries in the vector and outputs their values and indices as vectors. Thus `values[j]` is the `j`-th largest entry in `input`, and its index is `indices[j]`. For matrices (resp. higher rank input), computes the top `k` entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k] If two elements are equal, the lower-index element appears first. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 1-D or higher with last dimension at least `k`. k: A `Tensor`. Must be one of the following types: `int16`, `int32`, `int64`. 0-D. Number of top elements to look for along the last dimension (along each row for matrices). sorted: An optional `bool`. Defaults to `True`. If true the resulting `k` elements will be sorted by the values in descending order. index_type: An optional `tf.DType` from: `tf.int16, tf.int32, tf.int64`. Defaults to `tf.int32`. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (values, indices). values: A `Tensor`. Has the same type as `input`. indices: A `Tensor` of type `index_type`. r;r5r?N)r5r?r r!T)rrr5r?r r%Tk)r&rr'r(rr) _TopKV2Outputr+r,r-r.r/r0top_kv2_eager_fallbackr4r;r>rWrr{r?r@rArDrErFrG)rrr5r?r rHrIrJrKrLrMrNrOrPs rQtop_kv2rD 1s@    0h..0$ #\\  11 heQ&,g##G,g n ^ F   fh /&J!!*l;*'886j!QX QK' ""$**84c  %tS-?-?-EC..|<>F::L ,1    (' .9  & &- ##At,,  # #    # 6jtOO  # #   s03E00F7FF76F7;G G#"G#zraw_ops.TopKV2c|d}tj|d}|tj}tj|d}tj |g|tj tjtjtjtjtjtjtjtjtjtjtj g \}\}tj |g|tjtjtjgtj\}\}||g}d|d|d|d|f} tj"dd|| ||} tj$rtj&d || | t(j+| } | S) NTr5r?r%rAsTopKV2rSrTr;)r;r>rWrr{rVrZrurrrrrYrrXrrrrArGrBr+) rrr5r?r r!r[_attr_TkrPrOrJs rQrCrCS1s ^ F   fh /&J!!*l;*55ugsW__V]VeVegngtgtv}wDwDFMFSFSU\UaUacjcpcpryrBrBDKDRDRT[T`T`bibpbpryr@r@ECD'8E22A3gmmW]]\c\i\i=lnun{n{|.(DQ, fc7D(L  &   Y,f!$4 1' ""$ ,1    (' .r\)r#r$Tr#TN)r^N)rxN)N)r^riN)FFFrrN)FN)rrsr^TN)rr^TN)rrsrsrN)rN)FFrjN)TN(f__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrr&rr,rr;tensorflow.python.frameworkrrWtensorflow.security.fuzzing.pyr_atypesr _op_def_registryr r.r r?"tensorflow.python.util.deprecationr tensorflow.python.utilr r7 tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsr namedtupler*radd_fallback_dispatch_listadd_type_based_api_dispatcherintfloatboolr to_raw_opr_tf_type_based_dispatcherDispatchr1r3r]strrprdrjrwrrzr~rInt32rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr r r rrrr rrr"r#r%r)r*r,r4r/r3r6r?r8r=rErMrHrKrLrOrUrQrTrWrXr_rZr^rbrmrgrkrrrvrtrurxrrzrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrInt64rrrrrrrrrrrrrFloat32rrrrr%r r(rr&r/r+r,r1r*r0r:r7r8r<r5r;rBr?r@rDr>rCrLrIrJrNrHrMrQrXrUrWrZrar^r`rcBoolrkrgrirmrqrorpryrurvrZr|rsrzr~rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rrrrrrrrrrrrr#r!r"r+r&r'rr-r%r,r/r5r3r4rBr:rEr7rCr_rPrQrarLr`rqrhrirjrsrerrrrxryrzrrrwrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr#rrrr%rr$r1r(r)r*r+r,r3r'r2r<r6r7r8r>r5r=rGrArBrCrIr@rHrSrLrMrNrUrKrTr`rXrYrZr[rbrWrarxrfrgrhrir{rdryr}r~rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr rr r rrrrrrrrr!rrr r#r'r%r&r.r*r+r0r)r/r7r4r9r3r8rBr<r=r>rrDr;rCr"r\rQrbs 67179FK3IC86%%'*K** y +-?ARTbc %% (( >C #"67CCCUXCkpCAECjmCDHC)&CJ-Y+ ,^T^^L-I J +EENNyo1E'F3ehy~KOpsHL@~'9;LN_aop HIc</0H3H\_HvADFRARwSHT ')% &~t~~h'? 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