code: def create_model_inputs_onnx(
      input_infos: List[InputInfo],
  ) -> List[np.ndarray]:
      input_tensors = (
          np.random.randn(*input_info.size).astype(np.float32)
          if input_info.dtype is DataType.FLOAT32
          else np.random.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/onnx.py
imports: from typing import List, Tuple, Any, Optional, Dict
  import numpy
  from loguru import logger
  from nebullvm.config import ONNX_PROVIDERS
  from nebullvm.core.models import DeepLearningFramework, Device, DeviceType, InputInfo, DataType
  from nebullvm.optional_modules.onnx import onnx
  from nebullvm.optional_modules.onnxruntime import onnxruntime
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch
label_desc: Use image node_id 9 for calling a global function with example usage: create_model_inputs_onnx(input_infos) and returns: list