code: def convert_to_target_framework(
      tensor: np.ndarray, framework: DeepLearningFramework
  ) -> Any:
      if framework is DeepLearningFramework.PYTORCH:
          return torch.from_numpy(tensor)
      elif framework is DeepLearningFramework.TENSORFLOW:
          return tf.convert_to_tensor(tensor)
      else:
          return tensor
  
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 2 for calling a global function with example usage: convert_to_target_framework(tensor, framework) and returns: torch, tf, tensor