code: def get_numpy_to_framework_fn(arr) -> Callable:
      """
      Returns a function that converts a numpy array to the framework of the input array.
  
      Args:
          arr (`np.ndarray`): The array to convert.
      """
      if isinstance(arr, np.ndarray):
          return np.array
      if is_tf_available() and is_tf_tensor(arr):
          import tensorflow as tf
  
          return tf.convert_to_tensor
      if is_torch_available() and is_torch_tensor(arr):
          import torch
  
          return torch.tensor
      if is_flax_available() and is_jax_tensor(arr):
          import jax.numpy as jnp
  
          return jnp.array
      raise ValueError(f"Cannot convert arrays of type {type(arr)}")
  
imports: import io
  import pathlib
  from collections import defaultdict
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...feature_extraction_utils import BatchFeature
  from ...image_processing_utils import BaseImageProcessor, get_size_dict
  from ...image_transforms import PaddingMode, center_to_corners_format, corners_to_center_format, id_to_rgb, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, AnnotationType, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_annotations
  from ...utils import TensorType, is_flax_available, is_jax_tensor, is_scipy_available, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, logging
label_desc: Use image node_id 3 for calling a global function with example usage: get_numpy_to_framework_fn(arr) and returns: np, tf, torch, jnp