code: def pad_sequences(
      sequences,
      maxlen=None,
      dtype="int32",
      padding="post",
      truncating="post",
      value=0.0,
  ):
      """pad_sequences.
  
      Pad each sequence to the same length: the length of the longest sequence.
      If maxlen is provided, any sequence longer than maxlen is truncated to
      maxlen. Truncation happens off either the beginning or the end (default)
      of the sequence. Supports pre-padding and post-padding (default).
  
      Arguments:
          sequences: list of lists where each element is a sequence.
          maxlen: int, maximum length.
          dtype: type to cast the resulting sequence.
          padding: 'pre' or 'post', pad either before or after each sequence.
          truncating: 'pre' or 'post', remove values from sequences larger than
              maxlen either in the beginning or in the end of the sequence
          value: float, value to pad the sequences to the desired value.
  
      Returns:
          x: `numpy array` with dimensions (number_of_sequences, maxlen)
  
      Credits: From Keras `pad_sequences` function.
      """
      lengths = [len(s) for s in sequences]
  
      nb_samples = len(sequences)
      if maxlen is None:
          maxlen = np.max(lengths)
  
      x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
      for idx, s in enumerate(sequences):
          if len(s) == 0:
              continue  # empty list was found
          if truncating == "pre":
              trunc = s[-maxlen:]
          elif truncating == "post":
              trunc = s[:maxlen]
          else:
              raise ValueError(
                  "Truncating type '%s' not understood" % truncating
              )
  
          if padding == "post":
              x[idx, : len(trunc)] = trunc
          elif padding == "pre":
              x[idx, -len(trunc) :] = trunc
          else:
              raise ValueError(
                  "Padding type '%s' not understood" % padding
              )
      return x
  
imports: from __future__ import division, print_function, absolute_import
  import os
  import random
  import numpy
  from PIL import Image
  import pickle
  import csv
  import warnings
  import tensorflow.compat.v1
  from io import BytesIO
label_desc: Use image node_id 2 for calling a global function with example usage: pad_sequences(sequences, maxlen, dtype, padding, truncating, value) and returns: x