code: def aligned_array(size: int, dtype, align: int = 64) -> np.ndarray:
      """Returns an array of a given size that is 64-byte aligned.
  
      The returned array can be efficiently copied into GPU memory by TensorFlow.
  
      Args:
          size: The size (total number of items) of the array. For example,
              array([[0.0, 1.0], [2.0, 3.0]]) would have size=4.
          dtype: The numpy dtype of the array.
          align: The alignment to use.
  
      Returns:
          A np.ndarray with the given specifications.
      """
      n = size * dtype.itemsize
      empty = np.empty(n + (align - 1), dtype=np.uint8)
      data_align = empty.ctypes.data % align
      offset = 0 if data_align == 0 else (align - data_align)
      if n == 0:
          # stop np from optimising out empty slice reference
          output = empty[offset : offset + 1][0:0].view(dtype)
      else:
          output = empty[offset : offset + n].view(dtype)
  
      assert len(output) == size, len(output)
      assert output.ctypes.data % align == 0, output.ctypes.data
      return output
  
imports: from collections import OrderedDict
  from gymnasium.spaces import Discrete, MultiDiscrete
  import numpy
  import tree
  from types import MappingProxyType
  from typing import List, Optional
  from ray.rllib.utils.annotations import PublicAPI
  from ray.rllib.utils.deprecation import Deprecated
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.typing import SpaceStruct, TensorType, TensorStructType, Union
label_desc: Use image node_id 1 for calling a global function with example usage: aligned_array(size, dtype, align) and returns: output