code: def get_model_dummy_input(
      model,
      input_shape: Any,
      input_key: Union[str, List[str]],
      batchsize: int = 1,
      non_blocking: bool = False,
  ) -> Any:
      # input_shape with type dict of dict
      # e.g. {"key_1": {"key_1_1": [2, 3], "key_1_2": [4, 5, 6], "key_1_3": []}
      if isinstance(input_shape, dict):
          input = {}
          for key, value in input_shape.items():
              input[key] = get_model_dummy_input(
                  model, value, input_key, batchsize, non_blocking
              )
      elif isinstance(input_key, list):
          # av mode, with multiple input keys
          input = {}
          for i, key in enumerate(input_key):
              shape = (batchsize,) + tuple(input_shape[i])
              cur_input = torch.zeros(shape)
              if next(model.parameters()).is_cuda:
                  cur_input = cur_input.cuda(non_blocking=non_blocking)
              input[key] = cur_input
      else:
          # add a dimension to represent minibatch axis
          shape = (batchsize,) + tuple(input_shape)
          input = torch.zeros(shape)
          if next(model.parameters()).is_cuda:
              input = input.cuda(non_blocking=non_blocking)
          if input_key:
              input = {input_key: input}
      return input
  
imports: import collections.abc
  import contextlib
  import json
  import logging
  import os
  import time
  from functools import partial
  from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  import torch.nn
  from classy_vision.generic.distributed_util import broadcast_object, is_primary
  from fvcore.common.file_io import PathManager
label_desc: Use image node_id 26 for calling a global function with example usage: get_model_dummy_input(model, input_shape, input_key, batchsize, non_blocking) and returns: input