code: def zero_pad(
      batch: Sequence,
      zp_batch: Optional[np.ndarray] = None,
      dtype: type = np.float32,
      padding: Union[int, float] = 0,
  ) -> np.ndarray:
      """Fills the end of each array item to make its length maximal along each dimension.
  
      Args:
          batch: Initial array.
          zp_batch: Padded array.
          dtype = Type of padded array.
          padding = Number to will initial array with.
  
      Returns:
          Padded array.
  
      Examples:
          >>> x = np.array([[1, 2, 3], [4], [5, 6]])
          >>> zero_pad(x)
          array([[1., 2., 3.],
                 [4., 0., 0.],
                 [5., 6., 0.]], dtype=float32)
  
      """
      if zp_batch is None:
          dims = get_dimensions(batch)
          zp_batch = np.ones(dims, dtype=dtype) * padding
      if zp_batch.ndim == 1:
          zp_batch[: len(batch)] = batch
      else:
          for b, zp in zip(batch, zp_batch):
              zero_pad(b, zp)
      return zp_batch
  
imports: import collections
  import gzip
  import os
  import secrets
  import shutil
  import tarfile
  import zipfile
  from hashlib import md5
  from itertools import chain
  from logging import getLogger
  from pathlib import Path
  from typing import Any, Generator, Iterable, List, Mapping, Optional, Sequence, Sized, Union, Collection
  from urllib.parse import urlencode, parse_qs, urlsplit, urlunsplit, urlparse
  import numpy
  import requests
  from tqdm import tqdm
label_desc: Use image node_id 14 for calling a global function with example usage: zero_pad(batch, zp_batch, dtype, padding) and returns: zp_batch