parameters: (self,
                          input_tensors,
                          output_tensors,
                          input_masks,
                          output_masks,
                          arguments=None)
docstring: Internal method to create an inbound node for the layer.
  
      Arguments:
          input_tensors: list of input tensors.
          output_tensors: list of output tensors.
          input_masks: list of input masks (a mask can be a tensor, or None).
          output_masks: list of output masks (a mask can be a tensor, or None).
          arguments: dictionary of keyword arguments that were passed to the
              `call` method of the layer at the call that created the node.
docstring_summary: Internal method to create an inbound node for the layer.
docstring_tokens: Internal method to create an inbound node for the layer .
function: def _add_inbound_node(self,
                          input_tensors,
                          output_tensors,
                          input_masks,
                          output_masks,
                          arguments=None):
      """Internal method to create an inbound node for the layer.
  
      Arguments:
          input_tensors: list of input tensors.
          output_tensors: list of output tensors.
          input_masks: list of input masks (a mask can be a tensor, or None).
          output_masks: list of output masks (a mask can be a tensor, or None).
          arguments: dictionary of keyword arguments that were passed to the
              `call` method of the layer at the call that created the node.
      """
      input_tensors = _to_list(input_tensors)
      output_tensors = _to_list(output_tensors)
      input_masks = _to_list(input_masks)
      output_masks = _to_list(output_masks)
  
      # Collect input tensor(s) coordinates.
      inbound_layers = []
      node_indices = []
      tensor_indices = []
      for x in input_tensors:
        if hasattr(x, '_keras_history'):
          inbound_layer, node_index, tensor_index = x._keras_history
          inbound_layers.append(inbound_layer)
          node_indices.append(node_index)
          tensor_indices.append(tensor_index)
        else:
          inbound_layers.append(None)
          node_indices.append(None)
          tensor_indices.append(None)
  
      # Create node, add it to inbound nodes.
      Node(
          self,
          inbound_layers=inbound_layers,
          node_indices=node_indices,
          tensor_indices=tensor_indices,
          input_tensors=input_tensors,
          output_tensors=output_tensors,
          input_masks=input_masks,
          output_masks=output_masks,
          arguments=arguments)
  
      # Update tensor history and `_uses_learning_phase`.
      for i in range(len(output_tensors)):
        uses_lp = any(
            [getattr(x, '_uses_learning_phase', False) for x in input_tensors])
        uses_lp = getattr(self, 'uses_learning_phase', False) or uses_lp
        output_tensors[i]._uses_learning_phase = getattr(
            output_tensors[i], '_uses_learning_phase', False) or uses_lp
        output_tensors[i]._keras_history = (self, len(self.inbound_nodes) - 1, i)
function_tokens: def _add_inbound_node ( self , input_tensors , output_tensors , input_masks , output_masks , arguments = None ) : input_tensors = _to_list ( input_tensors ) output_tensors = _to_list ( output_tensors ) input_masks = _to_list ( input_masks ) output_masks = _to_list ( output_masks ) # Collect input tensor(s) coordinates. inbound_layers = [ ] node_indices = [ ] tensor_indices = [ ] for x in input_tensors : if hasattr ( x , '_keras_history' ) : inbound_layer , node_index , tensor_index = x . _keras_history inbound_layers . append ( inbound_layer ) node_indices . append ( node_index ) tensor_indices . append ( tensor_index ) else : inbound_layers . append ( None ) node_indices . append ( None ) tensor_indices . append ( None ) # Create node, add it to inbound nodes. Node ( self , inbound_layers = inbound_layers , node_indices = node_indices , tensor_indices = tensor_indices , input_tensors = input_tensors , output_tensors = output_tensors , input_masks = input_masks , output_masks = output_masks , arguments = arguments ) # Update tensor history and `_uses_learning_phase`. for i in range ( len ( output_tensors ) ) : uses_lp = any ( [ getattr ( x , '_uses_learning_phase' , False ) for x in input_tensors ] ) uses_lp = getattr ( self , 'uses_learning_phase' , False ) or uses_lp output_tensors [ i ] . _uses_learning_phase = getattr ( output_tensors [ i ] , '_uses_learning_phase' , False ) or uses_lp output_tensors [ i ] . _keras_history = ( self , len ( self . inbound_nodes ) - 1 , i )