AVh"$dZddlZddlmZddlmZddlmZddlm Z ddl m Z ddlmZdd l mZdd l mZdd l mZdd lmZdd lmZddlmZddlmZm Z m!Z!ddl"m#Z#ejHejJeddde#e!ejLfde'de'de#e!ejPffdZ)edejTe)Z+e)jXjZZ.de#e!ejLfde'de'de#e!ejPffdZ/y)zUPython wrappers around TensorFlow ops. This file is MACHINE GENERATED! Do not edit. N) pywrap_tfe)context)core)execute)dtypes)annotation_types)op_def_registry)ops)op_def_library)deprecated_endpoints)dispatch) tf_export)TypeVarListAny) Annotatedzio.encode_protosizes message_typedescriptor_sourcereturnc tjxstj}|j}|jr" t j |d|||d|d|d| }|St||||||fd}|tur|St/|t0t2fstd |z|D cgc]} t5j6| d}} t5j6|d}|d }t5j6|d} t9j:d||||||\} } } } | dd}t5j<rjd| j?dd| j?dd| j?dd | j?d f}| j@}t5jBd||||\}|S#t j$r } tj| |Yd} ~ nd} ~ wt j$rYnwxYw t||||||fd}|tur|St|||||||S#t j$rYtt f$rLt#j$t&dt)||||||}|t"j*j,ur|cYSwxYwcc} w#tt f$rLt#j$t&dt)||||||}|t"j*j,ur|cYSwxYw) a The op serializes protobuf messages provided in the input tensors. The types of the tensors in `values` must match the schema for the fields specified in `field_names`. All the tensors in `values` must have a common shape prefix, *batch_shape*. The `sizes` tensor specifies repeat counts for each field. The repeat count (last dimension) of a each tensor in `values` must be greater than or equal to corresponding repeat count in `sizes`. A `message_type` name must be provided to give context for the field names. The actual message descriptor can be looked up either in the linked-in descriptor pool or a filename provided by the caller using the `descriptor_source` attribute. For the most part, the mapping between Proto field types and TensorFlow dtypes is straightforward. However, there are a few special cases: - A proto field that contains a submessage or group can only be converted to `DT_STRING` (the serialized submessage). This is to reduce the complexity of the API. The resulting string can be used as input to another instance of the decode_proto op. - TensorFlow lacks support for unsigned integers. The ops represent uint64 types as a `DT_INT64` with the same twos-complement bit pattern (the obvious way). Unsigned int32 values can be represented exactly by specifying type `DT_INT64`, or using twos-complement if the caller specifies `DT_INT32` in the `output_types` attribute. The `descriptor_source` attribute selects the source of protocol descriptors to consult when looking up `message_type`. This may be: - An empty string or "local://", in which case protocol descriptors are created for C++ (not Python) proto definitions linked to the binary. - A file, in which case protocol descriptors are created from the file, which is expected to contain a `FileDescriptorSet` serialized as a string. NOTE: You can build a `descriptor_source` file using the `--descriptor_set_out` and `--include_imports` options to the protocol compiler `protoc`. - A "bytes://", in which protocol descriptors are created from ``, which is expected to be a `FileDescriptorSet` serialized as a string. Args: sizes: A `Tensor` of type `int32`. Tensor of int32 with shape `[batch_shape, len(field_names)]`. values: A list of `Tensor` objects. List of tensors containing values for the corresponding field. field_names: A list of `strings`. List of strings containing proto field names. message_type: A `string`. Name of the proto message type to decode. descriptor_source: An optional `string`. Defaults to `"local://"`. name: A name for the operation (optional). Returns: A `Tensor` of type `string`. EncodeProto field_namesrrN)rrrnamectx)rvaluesrrrrFExpected list for 'field_names' argument to 'encode_proto' Op, not %r.local:// Tinput_types)"_contextr_thread_local_datais_eagerrTFE_Py_FastPathExecute_core_NotOkStatusException_opsraise_from_not_ok_status_FallbackException_dispatcher_for_encode_protoNotImplementedencode_proto_eager_fallback_SymbolicException TypeError ValueError _dispatchr encode_protodict OpDispatcher NOT_SUPPORTED isinstancelisttuple_executemake_str_op_def_library_apply_op_helpermust_record_gradientget_attrinputsrecord_gradient)rrrrrr_ctxtld_resulte_s__op_outputs_attrs _inputs_flats Z/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/gen_encode_proto_ops.pyr1r1s2z    0h..0$ #\\ 11 mT5&- &9;LNgn8+  \3DdL Gn$ n K$ /  %'2 3 44ALL"""2}5L+L""<@,"''(9;NO )::U6{$0):GAq#x QK' ""$S\\-8.ll>*,?ll./ll>*,F::L |VW6 (' .I  & &- ##At,,  # #    , &+|5F  g  & ( [|-DdDD  # #  z "  "" "dv/:0<5F(, . g  ..<< <   ,M Z    DuV-8.:3D&* , Gi,,::: n   sV F%J 5!JG&2G  G&%G&*HHJ.AJJAK+)K+zraw_ops.EncodeProtoc@t|ttfstd|z|Dcgc]}t j |d}}t j |d}|d}t j |d}t j ||\}}tj|tj}|gt|z} d|d|d|d|f} t jdd| | || } t jrt jd | | | | \} | Scc}w) Nrrrrrr s EncodeProto)r>attrsrrr)r5r6r7r.r8r9convert_to_mixed_eager_tensorsr'convert_to_tensor_dtypesint32rr<r?) rrrrrrrrD_attr_Tinput_typesrIrHrBs rJr,r,s- K$ /  %'2 3 44ALL"""2}5L+L""<@,"''(9;NO'FFvsSf   6%4<', ; (.:L N&   ^Q|#)s ?' ""$ |VW6 (' .!MsD)rN)0__doc__ collectionstensorflow.pythonrtensorflow.python.eagerrr!rr%rr8tensorflow.python.frameworkrrPtensorflow.security.fuzzing.pyr_atypesr _op_def_registryr r'r r:"tensorflow.python.util.deprecationr tensorflow.python.utilr r0 tensorflow.python.util.tf_exportrtypingrrrtyping_extensionsradd_fallback_dispatch_listadd_type_based_api_dispatcherInt32strStringr1 to_raw_opr_tf_type_based_dispatcherDispatchr*r,rrJrisg 67179FK3IC86%%' %% (( F #w}}"45FZ]FqtFPYZ]_f_m_mZmPnF)&FP/i-.~t~~l/KL +EENNygmm1C'DilBEU^_bdkdrdr_rUsrh