code: def maybe_copy_np(arr):
      """Decide if we should make a copy of an array in order to release memory.
  
      NumPy arrays may be views into other array objects, by which a small array can
      maintain a persistent pointer to a large block of memory that prevents it from
      being garbage collected. This can quickly lead to memory issues as such
      blocks accumulate during inference.
  
      Args:
        arr: a NumPy array
  
      Returns:
        arr, or a copy of arr
      """
      if not isinstance(arr, np.ndarray):
          return arr
      # If this is not a view of another array.
      if arr.base is None:
          return arr
      # Heuristic to check if we should 'detach' this array from the parent blob.
      # We want to know if this array is a view that might leak memory.
      # The simplest check is if arr.base is larger than arr, but we don't want to
      # make unnecessary copies when this is just due to slicing along a batch,
      # because the other rows are likely still in use.
      # TODO(lit-dev): keep an eye on this, if we continue to have memory issues
      # we can make copies more aggressively.
      if arr.base.ndim > 1 and np.prod(arr.base.shape[1:]) > np.prod(
          arr.shape
      ):
          return np.copy(arr)
      # If only a batch slice, reshape, or otherwise.
      return arr
  
imports: import abc
  from collections.abc import Iterable, Iterator
  import inspect
  import itertools
  import multiprocessing
  from typing import Optional, Union
  from absl import logging
  from lit_nlp.api import dataset
  from lit_nlp.api import types
  from lit_nlp.lib import utils
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: maybe_copy_np(arr) and returns: arr, arr, arr, np