AVhDhdZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#ejHdgdZ%edddZ&edddddZ'd0de#e!e&fde#e!e'fde(de)de)f dZ*edejVe*Z,de#e!e&fde#e!e'fde(de)de)f d Z-ejHd!gdZ.ed"ddZ/ed#ddddZ0d0d$e#e!ejbfde#e!e/fde#e!e0fde(de)de)f d%Z2ed&ejVe2Z3d$e#e!ejbfde#e!e/fde#e!e0fde(de)de)f d'Z4ejHd(gdZ5ed)ddZ6ed*ddddZ7d0d+e#e!ejbfde#e!e6fd,e#e!ejbfde#e!e7fde(de)de)fd-Z8ed.ejVe8Z9d+e#e!ejbfde#e!e6fd,e#e!ejbfde#e!e7fde(de)de)fd/Z:y)1zUPython wrappers around TensorFlow ops. 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) AnnotatedDenseCountSparseOutput)output_indices output_valuesoutput_dense_shapeTV_DenseCountSparseOutput_Tz _atypes.Int32z _atypes.Int64%TV_DenseCountSparseOutput_output_typez_atypes.Float32z_atypes.Float64valuesweights binary_output minlength maxlengthc tjxstj}|j}|jr7 t j |d|||d|d|d| }t j|}|Stj |d}|d}tj"|d}|d}tj"|d}t%j&d||||||\} } } } | dd}tj(r{d | j+d d| j-dd| j-dd| j/dd | j+d f } | j0}tj2d|| |t j|}|S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t|||||||S#tj$rYwxYw) aPerforms sparse-output bin counting for a tf.tensor input. Counts the number of times each value occurs in the input. Args: values: A `Tensor`. Must be one of the following types: `int32`, `int64`. Tensor containing data to count. weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`. A Tensor of the same shape as indices containing per-index weight values. May also be the empty tensor if no weights are used. binary_output: A `bool`. Whether to output the number of occurrences of each value or 1. minlength: An optional `int` that is `>= -1`. Defaults to `-1`. Minimum value to count. Can be set to -1 for no minimum. maxlength: An optional `int` that is `>= -1`. Defaults to `-1`. Maximum value to count. Can be set to -1 for no maximum. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape). output_indices: A `Tensor` of type `int64`. output_values: A `Tensor`. Has the same type as `weights`. output_dense_shape: A `Tensor` of type `int64`. rrrrNrrrnamectx)rrrrrr T output_type)_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_DenseCountSparseOutputOutput_make_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException(dense_count_sparse_output_eager_fallback_SymbolicException_execute make_boolmake_int_op_def_library_apply_op_helpermust_record_gradient_get_attr_type _get_attr_int_get_attr_boolinputsrecord_gradient)rrrrrr _ctxtld_resulte__op_outputs_attrs _inputs_flats S/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_count_ops.pydense_count_sparse_outputrG s 4    0h..0$ #\\  11 &fg{; ?MKg.33G= -1`. Defaults to `-1`. Minimum value to count. Can be set to -1 for no minimum. maxlength: An optional `int` that is `>= -1`. Defaults to `-1`. Maximum value to count. Can be set to -1 for no maximum. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape). output_indices: A `Tensor` of type `int64`. output_values: A `Tensor`. Has the same type as `weights`. output_dense_shape: A `Tensor` of type `int64`. rUrrrNrr")rXrrrrrr r#r$)r%rr&r'rr(_RaggedCountSparseOutputOutputr*r+r,r-r.r/)ragged_count_sparse_output_eager_fallbackr1r2r3r4r5r6r7r8r9r:r;r<)rXrrrrrr r=r>r?r@rArBrCrDrEs rFragged_count_sparse_outputr\s8    0h..0$ #\\  11 'vvwY Yg/44W=g n$$]OD-I ;7)I ;7)'88!&+21>-6)(, .!QX QK' ""$3%%c*K ,k ,o  1=  / 1F ::L !<B * 0 0 9' .G  & &- ##At,,  # #    6 &'Y)%Dd<<  # #   s06FG!F<<GGG,,HHzraw_ops.RaggedCountSparseOutputc tj|d}|d}tj|d}|d}tj|d}tj|g|tj tj g\}\}tj|g|tj tj tjtjg\} \}tj|tj }|||g} d|d|d|d|d| f } tjdd| | || } tjrtjd | | | tj| } | S) Nrr"rrr#r$sRaggedCountSparseOutputrIrJrU)r2r3r4rLrMrNrOrPrQr-convert_to_tensorrr7r<rZr*) rXrrrrrr r!rRrSrErDr?s rFr[r[st$$]OD-I ;7)I ;7)66xw}}V]VcVcFfg'9F"*"A"A7)SSZS`S`biboboqxrArACJCRCRSU#VZg  ! !&'-- 8&&'*, +y+y=-1B D&   7$0C"& ('""$ !<B * 0 0 9' .rTSparseCountSparseOutputTV_SparseCountSparseOutput_T&TV_SparseCountSparseOutput_output_typeindices dense_shapectjxstj}|j} | jr9 t j |d|||||d|d|d| } t j| } | Stj |d}|d}tj"|d}|d}tj"|d}t%j&d|||||||| \} } } }|dd} tj(r{d | j+d d| j-dd| j-dd| j/dd | j+d f }| j0}tj2d||| t j| } | S#tj$r } tj| |Yd} ~ nd} ~ wtj$rYnwxYw t||||||||| S#tj$rYwxYw) aPerforms sparse-output bin counting for a sparse tensor input. Counts the number of times each value occurs in the input. Args: indices: A `Tensor` of type `int64`. Tensor containing the indices of the sparse tensor to count. values: A `Tensor`. Must be one of the following types: `int32`, `int64`. Tensor containing values of the sparse tensor to count. dense_shape: A `Tensor` of type `int64`. Tensor containing the dense shape of the sparse tensor to count. weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`. A Tensor of the same shape as indices containing per-index weight values. May also be the empty tensor if no weights are used. binary_output: A `bool`. Whether to output the number of occurrences of each value or 1. minlength: An optional `int` that is `>= -1`. Defaults to `-1`. Minimum value to count. Can be set to -1 for no minimum. maxlength: An optional `int` that is `>= -1`. Defaults to `-1`. Maximum value to count. Can be set to -1 for no maximum. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output_indices, output_values, output_dense_shape). output_indices: A `Tensor` of type `int64`. output_values: A `Tensor`. Has the same type as `weights`. output_dense_shape: A `Tensor` of type `int64`. r_rrrNrr")rbrrcrrrrr r#r$)r%rr&r'rr(_SparseCountSparseOutputOutputr*r+r,r-r.r/)sparse_count_sparse_output_eager_fallbackr1r2r3r4r5r6r7r8r9r:r;r<)rbrrcrrrrr r=r>r?r@rArBrCrDrEs rFsparse_count_sparse_outputrgs<    0h..0$ #\\  11 'w ii(g/44W=g n$$]OD-I ;7)I ;7)'88!76/:G1>-6)(, .!QX QK' ""$3%%c*K ,k ,o  1=  / 1F ::L !<B * 0 0 9' .I  & &- ##At,,  # #    6 6;9]  # #   s07FG#F>>GGG//HHzraw_ops.SparseCountSparseOutputc .tj|d}|d}tj|d}|d}tj|d}tj|g|tj tj g\} \}tj|g|tj tj tjtjg\} \}tj|tj }tj|tj }||||g} d| d|d|d|d| f } tjdd| | || } tjrtjd | | | tj| } | S) Nrr"rrr#r$sSparseCountSparseOutputrIrJr_)r2r3r4rLrMrNrOrPrQr-r^rr7r<rer*)rbrrcrrrrr r!rRrSrErDr?s rFrfrfFs$$]OD-I ;7)I ;7)66xw}}V]VcVcFfg'9F"*"A"A7)SSZS`S`biboboqxrArACJCRCRSU#VZg  " "7GMM :'&&{GMMB+6;8, +y+y=-1B D&   7$0C"& ('""$ !<B * 0 0 9' .rT)r"r"N);__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrr%rr+rr2tensorflow.python.frameworkrrMtensorflow.security.fuzzing.pyr_atypesr _op_def_registryr r-r r5"tensorflow.python.util.deprecationr tensorflow.python.utilr _dispatch tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsr namedtupler)rrboolintrG to_raw_oprr0rZrVrWInt64r\rUr[rer`rargr_rfrTrFr}s 67179FK3IC86%%' 6 6 6=!? &&C_Vef(/0WYjl}@OQ`)a%Ei5P0P&QE\efilQgQ]REcgEsvEEHENE#CD^T^^TmEnoYsD_?_5`ktux{`v`larvCFSV,"8!7!7="? 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