parameters: (self, name, input_name, output_name, output_shape)
return_statement: return spec_layer
docstring: Add a reshape_static layer to the model that reshapes a tensor.
          Refer to the **ReshapeStaticLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
          output_shape: list of int or tuple of int
              Target shape of the output tensor.
  
          See Also
          --------
          add_reshape, add_reshape_like, add_reshape_dynamic, add_rank_preserving_reshape
docstring_summary: Add a reshape_static layer to the model that reshapes a tensor.
          Refer to the **ReshapeStaticLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
docstring_tokens: Add a reshape_static layer to the model that reshapes a tensor . Refer to the ** ReshapeStaticLayerParams ** message in specification ( NeuralNetwork . proto ) for more details .
function: def add_reshape_static(self, name, input_name, output_name, output_shape):
          """
          Add a reshape_static layer to the model that reshapes a tensor.
          Refer to the **ReshapeStaticLayerParams** message in specification
          (NeuralNetwork.proto) for more details.
  
          Parameters
          ----------
          name: str
              The name of this layer.
          input_name: str
              The input blob name of this layer.
          output_name: str
              The output blob name of this layer.
          output_shape: list of int or tuple of int
              Target shape of the output tensor.
  
          See Also
          --------
          add_reshape, add_reshape_like, add_reshape_dynamic, add_rank_preserving_reshape
          """
  
          spec_layer = self._add_generic_layer(name, [input_name], [output_name])
          spec_layer_params = spec_layer.reshapeStatic
          spec_layer_params.targetShape.extend(output_shape)
          self.rank_dict[output_name] = len(output_shape)
          return spec_layer
function_tokens: def add_reshape_static ( self , name , input_name , output_name , output_shape ) : spec_layer = self . _add_generic_layer ( name , [ input_name ] , [ output_name ] ) spec_layer_params = spec_layer . reshapeStatic spec_layer_params . targetShape . extend ( output_shape ) self . rank_dict [ output_name ] = len ( output_shape ) return spec_layer