code: def get_dummy_input(dataset=None, device=None, input_shape=None):
      """Generate a representative dummy (random) input.
  
      If a device is specified, then the dummy_input is moved to that device.
  
      Args:
          dataset (str): Name of dataset from which to infer the shape
          device (str or torch.device): Device on which to create the input
          input_shape (tuple): Tuple of integers representing the input shape. Can also be a tuple of tuples, allowing
            arbitrarily complex collections of tensors. Used only if 'dataset' is None
      """
  
      def create_single(shape):
          t = torch.randn(shape)
          if device:
              t = t.to(device)
          return t
  
      def create_recurse(shape):
          if all(isinstance(x, int) for x in shape):
              return create_single(shape)
          return tuple(create_recurse(s) for s in shape)
  
      input_shape = input_shape or dataset.shape
      return create_recurse(input_shape)
  
imports: import argparse
  from collections import OrderedDict
  from copy import deepcopy
  import logging
  import operator
  import random
  import numpy
  import torch
  import torch.nn
  import torch.backends.cudnn
  import yaml
  import inspect
label_desc: Use image node_id 41 for calling a global function with example usage: get_dummy_input(dataset, device, input_shape) and returns: create_recurse, t, tuple, create_single