code: def create_input(shape, offset: int = 0):
      x = np.arange(np.prod(shape)).reshape(shape) + offset
  
      return torch.from_numpy(x).to(torch.get_default_dtype())
  
file_path: NeuralCompression/tests/utils.py
imports: from pathlib import Path
  from typing import Optional
  import numpy
  import torch
  from numpy.random import Generator
  from PIL import Image
label_desc: Use image node_id 1 for calling a global function with example usage: create_input(shape, offset) and returns: torch