code: def create_model_inputs_tf(
      input_infos: List[InputInfo],
  ) -> List[tf.Tensor]:
      return [
          tf.random_normal_initializer()(
              shape=(
                  input_info.size[0],
                  *input_info.size[2:],
                  input_info.size[1],
              )
          )
          if input_info.dtype is DataType.FLOAT32
          else tf.random.uniform(
              shape=(
                  input_info.size[0],
                  *input_info.size[2:],
                  input_info.size[1],
              ),
              minval=input_info.min_value or 0,
              maxval=input_info.max_value or 100,
              dtype=tf.int32,
          )
          for input_info in input_infos
      ]
  
file_path: nebuly/optimization/nebullvm/nebullvm/tools/tf.py
imports: from typing import Union, List, Tuple, Any, Optional, Dict
  import numpy
  from loguru import logger
  from nebullvm.core.models import Device, DataType, InputInfo
  from nebullvm.optional_modules.tensorflow import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: create_model_inputs_tf(input_infos) and returns: unknown