parameters: (receiver_tensors,
                               export_outputs,
                               receiver_tensors_alternatives=None,
                               serving_only=True)
return_statement: return signature_def_map
docstring: Build `SignatureDef`s for all export outputs.
  
    Args:
      receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
        input nodes where this receiver expects to be fed by default.  Typically,
        this is a single placeholder expecting serialized `tf.Example` protos.
      export_outputs: a dict of ExportOutput instances, each of which has
        an as_signature_def instance method that will be called to retrieve
        the signature_def for all export output tensors.
      receiver_tensors_alternatives: a dict of string to additional
        groups of receiver tensors, each of which may be a `Tensor` or a dict of
        string to `Tensor`.  These named receiver tensor alternatives generate
        additional serving signatures, which may be used to feed inputs at
        different points within the input receiver subgraph.  A typical usage is
        to allow feeding raw feature `Tensor`s *downstream* of the
        tf.io.parse_example() op.  Defaults to None.
      serving_only: boolean; if true, resulting signature defs will only include
        valid serving signatures. If false, all requested signatures will be
        returned.
  
    Returns:
      signature_def representing all passed args.
  
    Raises:
      ValueError: if export_outputs is not a dict
docstring_summary: Build `SignatureDef`s for all export outputs.
docstring_tokens: Build SignatureDef s for all export outputs .
function: def build_all_signature_defs(receiver_tensors,
                               export_outputs,
                               receiver_tensors_alternatives=None,
                               serving_only=True):
    """Build `SignatureDef`s for all export outputs.
  
    Args:
      receiver_tensors: a `Tensor`, or a dict of string to `Tensor`, specifying
        input nodes where this receiver expects to be fed by default.  Typically,
        this is a single placeholder expecting serialized `tf.Example` protos.
      export_outputs: a dict of ExportOutput instances, each of which has
        an as_signature_def instance method that will be called to retrieve
        the signature_def for all export output tensors.
      receiver_tensors_alternatives: a dict of string to additional
        groups of receiver tensors, each of which may be a `Tensor` or a dict of
        string to `Tensor`.  These named receiver tensor alternatives generate
        additional serving signatures, which may be used to feed inputs at
        different points within the input receiver subgraph.  A typical usage is
        to allow feeding raw feature `Tensor`s *downstream* of the
        tf.io.parse_example() op.  Defaults to None.
      serving_only: boolean; if true, resulting signature defs will only include
        valid serving signatures. If false, all requested signatures will be
        returned.
  
    Returns:
      signature_def representing all passed args.
  
    Raises:
      ValueError: if export_outputs is not a dict
    """
    if not isinstance(receiver_tensors, dict):
      receiver_tensors = {SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors}
    if export_outputs is None or not isinstance(export_outputs, dict):
      raise ValueError('export_outputs must be a dict and not'
                       '{}'.format(type(export_outputs)))
  
    signature_def_map = {}
    excluded_signatures = {}
    for output_key, export_output in export_outputs.items():
      signature_name = '{}'.format(output_key or 'None')
      try:
        signature = export_output.as_signature_def(receiver_tensors)
        signature_def_map[signature_name] = signature
      except ValueError as e:
        excluded_signatures[signature_name] = str(e)
  
    if receiver_tensors_alternatives:
      for receiver_name, receiver_tensors_alt in (
          receiver_tensors_alternatives.items()):
        if not isinstance(receiver_tensors_alt, dict):
          receiver_tensors_alt = {
              SINGLE_RECEIVER_DEFAULT_NAME: receiver_tensors_alt
          }
        for output_key, export_output in export_outputs.items():
          signature_name = '{}:{}'.format(receiver_name or 'None', output_key or
                                          'None')
          try:
            signature = export_output.as_signature_def(receiver_tensors_alt)
            signature_def_map[signature_name] = signature
          except ValueError as e:
            excluded_signatures[signature_name] = str(e)
  
    _log_signature_report(signature_def_map, excluded_signatures)
  
    # The above calls to export_output_lib.as_signature_def should return only
    # valid signatures; if there is a validity problem, they raise a ValueError,
    # in which case we exclude that signature from signature_def_map above.
    # The is_valid_signature check ensures that the signatures produced are
    # valid for serving, and acts as an additional sanity check for export
    # signatures produced for serving. We skip this check for training and eval
    # signatures, which are not intended for serving.
    if serving_only:
      signature_def_map = {
          k: v
          for k, v in signature_def_map.items()
          if signature_def_utils.is_valid_signature(v)
      }
    return signature_def_map
function_tokens: def build_all_signature_defs ( receiver_tensors , export_outputs , receiver_tensors_alternatives = None , serving_only = True ) : if not isinstance ( receiver_tensors , dict ) : receiver_tensors = { SINGLE_RECEIVER_DEFAULT_NAME : receiver_tensors } if export_outputs is None or not isinstance ( export_outputs , dict ) : raise ValueError ( 'export_outputs must be a dict and not' '{}' . format ( type ( export_outputs ) ) ) signature_def_map = { } excluded_signatures = { } for output_key , export_output in export_outputs . items ( ) : signature_name = '{}' . format ( output_key or 'None' ) try : signature = export_output . as_signature_def ( receiver_tensors ) signature_def_map [ signature_name ] = signature except ValueError as e : excluded_signatures [ signature_name ] = str ( e ) if receiver_tensors_alternatives : for receiver_name , receiver_tensors_alt in ( receiver_tensors_alternatives . items ( ) ) : if not isinstance ( receiver_tensors_alt , dict ) : receiver_tensors_alt = { SINGLE_RECEIVER_DEFAULT_NAME : receiver_tensors_alt } for output_key , export_output in export_outputs . items ( ) : signature_name = '{}:{}' . format ( receiver_name or 'None' , output_key or 'None' ) try : signature = export_output . as_signature_def ( receiver_tensors_alt ) signature_def_map [ signature_name ] = signature except ValueError as e : excluded_signatures [ signature_name ] = str ( e ) _log_signature_report ( signature_def_map , excluded_signatures ) # The above calls to export_output_lib.as_signature_def should return only # valid signatures; if there is a validity problem, they raise a ValueError, # in which case we exclude that signature from signature_def_map above. # The is_valid_signature check ensures that the signatures produced are # valid for serving, and acts as an additional sanity check for export # signatures produced for serving. We skip this check for training and eval # signatures, which are not intended for serving. if serving_only : signature_def_map = { k : v for k , v in signature_def_map . items ( ) if signature_def_utils . is_valid_signature ( v ) } return signature_def_map