code: def _print_layer_type_and_arguments(
      layer_type_str,
      layer_inputs,
      indentation,
      to_indent=True,
      shape=None,
      value=None,
  ):
      if to_indent:
          _prRed(
              indentation * "\t" + "{}".format(layer_type_str), end=""
          )
      else:
          _prRed("{}".format(layer_type_str), end="")
  
      if shape is None:
          _prLightPurple("({})".format(", ".join(layer_inputs)))
      elif value is not None:
          _prLightPurple("(shape = ", end="")
          print("{}, ".format(str(shape)), end="")
          _prLightPurple("value = ", end="")
          values = ",".join(
              ["{0: 0.1f}".format(v) for v in value]
          ).lstrip()
          print("[{}]".format(values), end="")
          _prLightPurple(")")
      else:
          _prLightPurple("(shape = ", end="")
          print("{}".format(str(shape)), end="")
          _prLightPurple(")")
  
file_path: coremltools/coremltools/models/neural_network/spec_inspection_utils.py
imports: from ...proto import NeuralNetwork_pb2
label_desc: Use image node_id 10 for calling a global function with example usage: _print_layer_type_and_arguments(layer_type_str, layer_inputs, indentation, to_indent, shape, value) without return types