code: def create_model_inputs_torch(
      input_infos: List[InputInfo],
  ) -> List[torch.Tensor]:
      input_tensors = (
          torch.randn(*input_info.size)
          if input_info.dtype is DataType.FLOAT32
          else torch.randint(
              size=input_info.size,
              low=input_info.min_value or 0,
              high=input_info.max_value or 100,
          )
          for input_info in input_infos
      )
      return list(input_tensors)
  
file_path: nebuly/optimization/nebullvm/nebullvm/tools/pytorch.py
imports: from pathlib import Path
  from typing import List, Tuple, Optional, Dict, Union, Sequence
  from loguru import logger
  from nebullvm.core.models import Device, DataType, DeviceType, InputInfo
  from nebullvm.optional_modules.torch import torch, DataLoader
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.diffusers import get_default_dynamic_info
label_desc: Use image node_id 4 for calling a global function with example usage: create_model_inputs_torch(input_infos) and returns: list