docstr: Creates a tuple of input arrays with correct shapes that can be used for shape inference
  of the model weights and for printing the summary
  :return: tuple of inputs for model forward pass
docstr_tok: ["Creates","a","tuple","of","input","arrays","with","correct","shapes","that","can","be","used","for","shape","inference","of","the","model","weights","and","for","printing","the","summary",":","return",":","tuple","of","inputs","for","model","forward","pass"]
code: def _dummy_model_inputs(self) -> Tuple[NDArray, ...]:
      """
      Creates a tuple of input arrays with correct shapes that can be used for shape inference
      of the model weights and for printing the summary
      :return: tuple of inputs for model forward pass
      """
      input_shapes = self._model_input_shapes()
      inputs = tuple(
          nd.zeros(tuple(shape), ctx=self._devices[0])
          for shape in input_shapes
      )
      return inputs
  
imports: import copy
  from typing import Any, Dict, Generator, List, Tuple, Union
  import numpy
  import mxnet
  from mxnet import autograd, gluon, nd
  from mxnet.ndarray import NDArray
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION
  from rl_coach.architectures.mxnet_components import utils
  from rl_coach.architectures.mxnet_components.savers import ParameterDictSaver, OnnxSaver
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.logger import screen
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list
label_desc: Use image node_id 6 for calling the MxnetArchitecture obj's underlying member method code with example usage: obj._dummy_model_inputs() and returns: inputs