code: def pad_sequence_input(
      x: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray]:
      """
      Apply padding to a batch of 1-dimensional samples such that it has shape of (batch_size, max_length).
  
      :param x: A batch of 1-dimensional input data, e.g. `np.array([np.array([1,2,3]), np.array([4,5,6,7])])`.
      :return: The padded input batch and its corresponding mask.
      """
      max_length = max(map(len, x))
      batch_size = x.shape[0]
  
      # note: use dtype of inner elements
      x_padded = np.zeros((batch_size, max_length), dtype=x[0].dtype)
      x_mask = np.zeros((batch_size, max_length), dtype=bool)
  
      for i, x_i in enumerate(x):
          x_padded[i, : len(x_i)] = x_i
          x_mask[i, : len(x_i)] = 1
      return x_padded, x_mask
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import math
  import os
  import shutil
  import sys
  import tarfile
  import warnings
  import zipfile
  from functools import wraps
  from inspect import signature
  from typing import TYPE_CHECKING, Callable, List, Dict, Optional, Tuple, Union
  import numpy
  import six
  from scipy.special import gammainc
  from tqdm.auto import tqdm
  from art import config
label_desc: Use image node_id 44 for calling a global function with example usage: pad_sequence_input(x) and returns: x_padded,  x_mask