AVhd dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#edZ$edZ%e jLdfde#e!e%fde$de#e!e$ffdZ'edejPe'Z)de#e!e%fde$de#e!e$ffdZ*dPde#e!ejVfde#e!ejXfde#e!ejZfde#e!ej\ffdZ/edejPe/Z0de#e!ejVfde#e!ejXfde#e!ejZfde#e!ej\ffdZ1dPde#e!ejVfde#e!ej\fde#e!ej\ffd Z2ed!ejPe2Z3de#e!ejVfde#e!ej\fde#e!ej\ffd"Z4ed#d$d%Z5ed&d'd(d)d$d%Z6ed*d'd(d)d$d%Z7e jLdfde#e!ejVfde#e!ej\fde#e!e5fd+e#e!e6fd,e#e!e6fde7de#e!e7ffd-Z8ed.ejPe8Z9de#e!ejVfde#e!ej\fde#e!e5fd+e#e!e6fd,e#e!e6fde7de#e!e7ffd/Z:ed0Z;ed1Zed3ejPe>Z?de#e!ejVfde#e!eZJed?ZKed@ZLe jzdfde#e!ejVfde#e!ej\fde#e!eLfdeKde#e!eKff dAZMedBejPeMZNde#e!ejVfde#e!ej\fde#e!eLfdeKde#e!eKff dCZOedDZPedEZQe jdfde#e!ejVfde#e!ej\fde#e!eQfdePde#e!ePff dFZSedGejPeSZTde#e!ejVfde#e!ej\fde#e!eQfdePde#e!ePff dHZUedIZVedJZWdPde#e!ejVfde#e!ej\fde#e!eWfdKe#e!eVfdLe#e!eVfde#e!eVff dMZXedNejPeXZYde#e!ejVfde#e!ej\fde#e!eWfdKe#e!eVfdLe#e!eVfde#e!eVff dOZZy)QzUPython 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) Annotated) TV_NonDeterministicInts_dtype_atypes.BFloat16 _atypes.Bool_atypes.Complex128_atypes.Complex64_atypes.Float16_atypes.Float32_atypes.Float64_atypes.Float8e4m3b11fnuz_atypes.Float8e4m3fn_atypes.Float8e4m3fnuz_atypes.Float8e5m2_atypes.Float8e5m2fnuz _atypes.Half _atypes.Int16 _atypes.Int32 _atypes.Int4 _atypes.Int64 _atypes.Int8_atypes.QInt16_atypes.QInt32 _atypes.QInt8_atypes.QUInt16_atypes.QUInt8_atypes.Resource_atypes.String_atypes.UInt16_atypes.UInt32 _atypes.UInt4_atypes.UInt64 _atypes.UInt8_atypes.Variant) #TV_NonDeterministicInts_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2shapedtypereturnctjxstj}|j}|jr t j |d||d|}|S|tj}tj |d}t#j$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$rYwxYw)aNon-deterministically generates some integers. This op may use some OS-provided source of non-determinism (e.g. an RNG), so each execution will give different results. Args: shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.int64`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. NonDeterministicIntsr5Nr5namectx)r4r5r: shape_dtype)_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException%non_deterministic_ints_eager_fallback_SymbolicException_dtypesint64_execute make_type_op_def_library_apply_op_helpermust_record_gradient_get_attr_typeinputsrecord_gradient) r4r5r:_ctxtld_resulte__op_outputs_attrs _inputs_flats ]/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_stateful_random_ops.pynon_deterministic_intsr\st    0h..0$ #\\ 11 $dE7ECg n ] MME   UG ,%'88e5tE!QX QK' ""$s))'2M  /1F::L  fg? (' ./  & &- ##At,,  # #    2 u4T33  # #   s0D ED77EEE##E:9E:zraw_ops.NonDeterministicIntscV|tj}tj|d}tj|g|gtj\}\}|g}d|d|f}tj dd||||}tj rtjd||||\}|S)Nr5r<sNonDeterministicIntsrPattrsr;r:r8)rHrIrJrKargs_to_matching_eagerrrNrQ)r4r5r:r;_attr_shape_dtyperZrYrTs r[rFrFMs ] MME   UG ,% ( ? ?bRYR_R_ `Xe, UM+< =&   4a #)s ?' ""$  fg? (' .resourcealgdeltachtjxstj}|j}|jr t j |d||||}|Stjd||||\}}} } | dd}tj r&d} | j"} tj$d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)aAdvance the counter of a counter-based RNG. The state of the RNG after `rng_read_and_skip(n)` will be the same as that after `uniform([n])` (or any other distribution). The actual increment added to the counter is an unspecified implementation choice. In the case that the input algorithm is RNG_ALG_AUTO_SELECT, the counter in the state needs to be of size int64[2], the current maximal counter size among algorithms. In this case, this op will manage the counter as if it is an 128-bit integer with layout [lower_64bits, higher_64bits]. If an algorithm needs less than 128 bits for the counter, it should use the left portion of the int64[2]. In this way, the int64[2] is compatible with all current RNG algorithms (Philox, ThreeFry and xla::RandomAlgorithm::RNG_DEFAULT). Downstream RNG ops can thus use this counter with any RNG algorithm. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. The state consists of the counter followed by the key. alg: A `Tensor` of type `int32`. The RNG algorithm. delta: A `Tensor` of type `uint64`. The amount of advancement. name: A name for the operation (optional). Returns: A `Tensor` of type `int64`. RngReadAndSkipNr:r;)rdrerfr:)r=rr>r?rr@rArBrCrDrE rng_read_and_skip_eager_fallbackrGrLrMrJrNrPrQ) rdrerfr:rRrSrTrUrVrWrXrYrZs r[rng_read_and_skiprl]s5(    0h..0$ #\\ 11 hU.> ?( sGMM2#   7%C', &   .,#)s ?' ""$ ,9 (' .rc algorithmctjxstj}|j}|jr t j |d||||}|Stjd||||\}}} } | S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)a_Advance the counter of a counter-based RNG. The state of the RNG after `rng_skip(n)` will be the same as that after `stateful_uniform([n])` (or any other distribution). The actual increment added to the counter is an unspecified implementation detail. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. delta: A `Tensor` of type `int64`. The amount of advancement. name: A name for the operation (optional). Returns: The created Operation. RngSkipNri)rdrqrfr:)r=rr>r?rr@rArBrCrDrErng_skip_eager_fallbackrGrLrM) rdrqrfr:rRrSrTrUrVrWrXs r[rng_skiprus$    0h..0$ #\\ 11 ixE;g n(88H !QX *  & &- ##At,,  # #    $ Iu4T;;  # #   s0A==CB++CCCC.-C.zraw_ops.RngSkipc&tj|tj}tj|tj}tj|tj}|||g}d}t j dd||||}d}|S)NsRngSkiprr_)rCrnrHrdrIrJr)rdrqrfr:r;rZrYrTs r[rtrts~  # #Hg.>.> ?($$Y >)   6%Iu-, &   Zr?rr@rArBrCrDrE'stateful_random_binomial_eager_fallbackrGrHrIrJrKrLrMrNrOrPrQ)rdrqr4rzr{r5r:rRrSrTrUrVrWrXrYrZs r[stateful_random_binomialrs    0h..0$ #\\ 11 &h 5w'gn ] MME   UG ,%'88 8y(-fE(-D:!QX QK' ""$3%%c*C1C1CC1Hs))'24F::L  ,A (' .5  & &- ##At,,  # #    4 Iufe5t  # #   s0D""E)5EE)(E)-FFFzraw_ops.StatefulRandomBinomialc |tj}tj|d}tj|g|tj tjg\}\}tj||g|tj tjtjtj tjgtj\} } | \}}tj|tj}tj|tj}|||||g} d|d| d|f} tjdd| | ||} tjrtjd| | | | \} | S)Nr5r~rsStatefulRandomBinomialr^r_r})rHrIrJrKrarohalffloat32float64rCrnrdrrNrQ)rdrqr4rzr{r5r:r;_attr_S_attr_T _inputs_TrZrYrTs r[rrsu ] MME   UG ,%55ugsW]]T[TaTaDde'8E66gll\c\k\kmtm|m|FLLNUN[N[N^`g`o`op'9/65  # #Hg.>.> ?($$Y >)Iufe<, #w 7&   6$0C"& ('""$  ,A (' .rc) TV_StatefulStandardNormal_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) %TV_StatefulStandardNormal_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2c tjxstj}|j}|jr t j |d|||d|}|S|tj}tj |d}t#j$d||||\}}} } | dd}tj&rHd| j)dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t|||||S#t j$rYwxYw)a&Outputs random values from a normal distribution. This op is deprecated in favor of op 'StatefulStandardNormalV2' The generated values will have mean 0 and standard deviation 1. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.float32`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. StatefulStandardNormalr5Nr9)rdr4r5r:r<)r=rr>r?rr@rArBrCrDrE'stateful_standard_normal_eager_fallbackrGrHrrJrKrLrMrNrOrPrQ) rdr4r5r:rRrSrTrUrVrWrXrYrZs r[stateful_standard_normalr's}    0h..0$ #\\ 11 &hwOg n ] OOE   UG ,%'88 85'+-!QX QK' ""$s))'2M  /1F::L  ,A (' .1  & &- ##At,,  # #    4 ETt==  # #   s0D ED99EEE&&E=<E=zraw_ops.StatefulStandardNormalc|tj}tj|d}tj|g|gtj \}\}t j|tj}||g}d|d|f}tjdd||||}tjrtjd||||\}|S)Nr5r<sStatefulStandardNormalr^r_r rHrrJrKrarIrCrnrdrrNrQ) rdr4r5r:r;rbrZrYrTs r[rr[s ] OOE   UG ,% ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?(E", UM+< =&   6$0C"& ('""$  ,A (' .rc) !TV_StatefulStandardNormalV2_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) 'TV_StatefulStandardNormalV2_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2c tjxstj}|j}|jr t j |d||||d|}|S|tj}tj |d}t#j$d|||||\} } } } | dd}tj&rHd| j)dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY!wxYw)a$Outputs random values from a normal distribution. The generated values will have mean 0 and standard deviation 1. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.float32`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. StatefulStandardNormalV2r5Nr9rdrqr4r5r:r<)r=rr>r?rr@rArBrCrDrE*stateful_standard_normal_v2_eager_fallbackrGrHrrJrKrLrMrNrOrPrQrdrqr4r5r:rRrSrTrUrVrWrXrYrZs r[stateful_standard_normal_v2rps"    0h..0$ #\\ 11 ($)Ugn ] OOE   UG ,%'88"X*/u4I!QX QK' ""$s))'2M  /1F::L "L&'C (' .1  & &- ##At,,  # #    7 IuE$HH  # #   0D E D;;EEE))F?Fz raw_ops.StatefulStandardNormalV2c|tj}tj|d}tj|g|gtj \}\}t j|tj}t j|tj }|||g}d|d|f}tjdd||||} tjrtjd||| | \} | S)Nr5r<sStatefulStandardNormalV2r^r_rr rdrqr4r5r:r;rbrZrYrTs r[rrs ] OOE   UG ,% ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?($$Y >)Iu-, UM+< =&   8!$0C"& ('""$ "L&'C (' .rc) TV_StatefulTruncatedNormal_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) &TV_StatefulTruncatedNormal_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2c tjxstj}|j}|jr t j |d||||d|}|S|tj}tj |d}t#j$d|||||\} } } } | dd}tj&rHd| j)dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY!wxYw)aOutputs random values from a truncated normal distribution. The generated values follow a normal distribution with mean 0 and standard deviation 1, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.float32`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. StatefulTruncatedNormalr5Nr9rr<)r=rr>r?rr@rArBrCrDrE(stateful_truncated_normal_eager_fallbackrGrHrrJrKrLrMrNrOrPrQrs r[stateful_truncated_normalrs&    0h..0$ #\\ 11 'xEgn ] OOE   UG ,%'88!H ).e$H!QX QK' ""$s))'2M  /1F::L !<B (' .1  & &- ##At,,  # #    5 IuE$HH  # #   rzraw_ops.StatefulTruncatedNormalc|tj}tj|d}tj|g|gtj \}\}t j|tj}t j|tj }|||g}d|d|f}tjdd||||} tjrtjd||| | \} | S)Nr5r<sStatefulTruncatedNormalr^r_rrrs r[rrs ] OOE   UG ,% ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?($$Y >)Iu-, UM+< =&   7$0C"& ('""$ !<B (' .rc) TV_StatefulUniform_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) TV_StatefulUniform_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2c tjxstj}|j}|jr t j |d||||d|}|S|tj}tj |d}t#j$d|||||\} } } } | dd}tj&rHd| j)dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY!wxYw)aOutputs random values from a uniform distribution. The generated values follow a uniform distribution in the range `[0, 1)`. The lower bound 0 is included in the range, while the upper bound 1 is excluded. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.float32`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. StatefulUniformr5Nr9rr<)r=rr>r?rr@rArBrCrDrEstateful_uniform_eager_fallbackrGrHrrJrKrLrMrNrOrPrQrs r[stateful_uniformr s$    0h..0$ #\\ 11 xE7 gn ] OOE   UG ,%'88H !&e$@!QX QK' ""$s))'2M  /1F::L <: (' .1  & &- ##At,,  # #    , IuE$HH  # #   rzraw_ops.StatefulUniformc|tj}tj|d}tj|g|gtj \}\}t j|tj}t j|tj }|||g}d|d|f}tjdd||||} tjrtjd||| | \} | S)Nr5r<sStatefulUniformr^r_rrrs r[rrAs ] OOE   UG ,% ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?($$Y >)Iu-, UM+< =&   /<#)s ?' ""$ <: (' .rc) TV_StatefulUniformFullInt_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) %TV_StatefulUniformFullInt_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2c tjxstj}|j}|jr t j |d||||d|}|S|tj}tj |d}t#j$d|||||\} } } } | dd}tj&rHd| j)dd| j)df} | j*} tj,d| | ||\}|S#t j$r }tj||Yd}~nd}~wt j$rYnwxYw t||||||S#t j$rY!wxYw)a5Outputs random integers from a uniform distribution. The generated values are uniform integers covering the whole range of `dtype`. Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. shape: A `Tensor`. The shape of the output tensor. dtype: An optional `tf.DType`. Defaults to `tf.uint64`. The type of the output. name: A name for the operation (optional). Returns: A `Tensor` of type `dtype`. StatefulUniformFullIntr5Nr9rr<)r=rr>r?rr@rArBrCrDrE(stateful_uniform_full_int_eager_fallbackrGrHrprJrKrLrMrNrOrPrQrs r[stateful_uniform_full_intrVs"    0h..0$ #\\ 11 &h 5gn ] NNE   UG ,%'88 8y(-UG!QX QK' ""$s))'2M  /1F::L  ,A (' .1  & &- ##At,,  # #    5 IuE$HH  # #   rzraw_ops.StatefulUniformFullIntc|tj}tj|d}tj|g|gtj \}\}t j|tj}t j|tj }|||g}d|d|f}tjdd||||} tjrtjd||| | \} | S)Nr5r<sStatefulUniformFullIntr^r_r) rHrprJrKrarIrCrnrdrrNrQrs r[rrs ] NNE   UG ,% ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?($$Y >)Iu-, UM+< =&   6$0C"& ('""$  ,A (' .rc) TV_StatefulUniformInt_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2) !TV_StatefulUniformInt_shape_dtyperrrrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+r,r-r.r/r0r1r2minvalmaxvalc tjxstj}|j}|jr t j |d||||||}|Stjd||||||\} } } } | 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)aOutputs random integers from a uniform distribution. The generated values are uniform integers in the range `[minval, maxval)`. The lower bound `minval` is included in the range, while the upper bound `maxval` is excluded. The random integers are slightly biased unless `maxval - minval` is an exact power of two. The bias is small for values of `maxval - minval` significantly smaller than the range of the output (either `2^32` or `2^64`). Args: resource: A `Tensor` of type `resource`. The handle of the resource variable that stores the state of the RNG. algorithm: A `Tensor` of type `int64`. The RNG algorithm. shape: A `Tensor`. The shape of the output tensor. minval: A `Tensor`. Minimum value (inclusive, scalar). maxval: A `Tensor`. Must have the same type as `minval`. Maximum value (exclusive, scalar). name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `minval`. StatefulUniformIntNri)rdrqr4rrr:r5r<)r=rr>r?rr@rArBrCrDrE#stateful_uniform_int_eager_fallbackrGrLrMrJrNrOrPrQ)rdrqr4rrr:rRrSrTrUrVrWrXrYrZs r[stateful_uniform_intrsk0    0h..0$ #\\ 11 "D(Iufgn(88x9$)&#')!QX QK' ""$s))'2M  /1F::L lFG= (' .-  & &- ##At,,  # #    0 Iuff4TKK  # #   s0C&&D-9DD-,D-1EEEzraw_ops.StatefulUniformIntctj||g|gtj\}}|\}}tj|g|gtj\} \}t j |tj }t j |tj}|||||g} d|d| f} tjdd| | ||} tjrtjd| | | | \} | S)Nr5r<sStatefulUniformIntr^r_r) rJrarHrIrCrnrdrrNrQ) rdrqr4rrr:r; _attr_dtype _inputs_dtyperbrZrYrTs r[rrs'>>?OQTVXZaZgZgh+}"66 ( ? ?bRYR_R_ `Xe  # #Hg.>.> ?($$Y >)Iuff=, [-1B C&   2Al#)s ?' ""$ lFG= (' .rc)N)[__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrr=rrArrJtensorflow.python.frameworkrrHtensorflow.security.fuzzing.pyr_atypesr _op_def_registryr rCr rL"tensorflow.python.util.deprecationr tensorflow.python.utilr _dispatch tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsrrr3rIr\ to_raw_opr8rFResourceInt32UInt64Int64rlrhrkrursrtrwrxryrr}rrrrrrrrrrrrrrrrrrrrrrrrrprrrrrrrrrjrcr[rs 67179FK3IC86%%' '!^ &-'j #|C|I|IPT,)C1T,T"U,]z,YbcfhEcEYF,\Ay!?@PfAgh 3@c;c1d nK [dehjGeG[H  . #w/?/?*? @.yQTV]VcVcQcGd.mvwz}D}K}KxKnL.\efikrkxkxfx\y.`534^T^^DU5VW yg>N>N9N/O V_`celerer`rVs }FGJLSLZLZGZ}[ ktuxzAzGzGuGkH & yg&6&6!67& IcSZS`S`N`Da& jstwzAzGzGuGkH& N ')% &~t~~h'? @ iW5E5E0E&F S\]`bibobo]oSp zCDGIPIVIVDVzW &&C_Vef%&CEVXiky|KM\]")*KM^`qtBDSUd#ew~wDwDKO1yg6F6F1F'G1T]^acjcpcp^pTq1{DEHJeEe{f1pyz}ZzZp[1dmnqsNnNdO1Wv1T]^acB^BTC1fE#CD^T^^TlEmniWEUEU@U6VclmpryrrmdAJSTWYtTtJuHILNiIijs|}@B]}]s^gFV_`ceD`DVE*#*#b (/)n %nun}n}DH/yg6F6F1F'G/PYZ]`E[EQF/Nm/MVWZ\{W{M|/bE#CD^T^^TlEmniWEUEU@U6V_hiloTjT`U^}MVWZ\{W{M|$%,%f !*1+r '_f_n_nuy1)C9I9I4I*J1W`adfmfsfsasWt1~GHKMtHt~u1}^1~GHKMnHn~o1fI9%GHXsItu3HXHXCX9Yfopsu|vCvCqCgDMVWZ\CWCMDMn~GHKMnHn~o&$+$d )0*p &[b[j[jqu3 #w7G7G2G(H3U^_bdkdqdq_qUr3|EFIKqFq|r3zZ3zCDGIiDizj3jG)$EF~t~~VoGpqygFVFVAV7WdmnqsztAtAoAeBKTUXZ@U@KAJjzCDGIiDizj&#T !("` BIBQBQX\2yg.>.>)>?2IVY[b[h[hVhLi2r{|B`}`sa2iA2ajknpHkHaI2h7)56~t~~FV7WXiW=M=M8M.N[dehjqjwjwew[xBKLOQoLoBpyQajknpHkHaI$#*#b (/)n %Y`YgYgnr1 #w7G7G2G(H1U^_bdkdqdq_qUr1|EFIKpFp|q1yX1w@ADFeAewf1fE#CD^T^^TmEnoygFVFVAV7WdmnqsztAtAoAeBKTUXZUK@IhxABEGfBfxg&&Z $+%f !69S'2B2B-B#C6PYZ]_f_l_lZlPm6vADFgAgwh6r{|A\|\r]6gpqtvQqQgR6bkloqLlLbM6p=Y;<^T^^L`=ab)CAQAQ