code: def combine_tensor_batch(tensor, lengths, max_length, batch_size):
      """Combine a batch of variable-length tensors into a single tensor."""
      combined = tf.concat(
          [tensor[i, : lengths[i]] for i in range(batch_size)], axis=0
      )
      final_length = max_length * batch_size
      combined_padded = tf.pad(
          combined,
          [(0, final_length - tf.shape(combined)[0])]
          + [(0, 0)] * (combined.shape.rank - 1),
      )
      combined_padded.set_shape(
          [final_length] + combined_padded.shape[1:]
      )
      return combined_padded
  
imports: import collections
  import functools
  import io
  import re
  import wave
  import zlib
  import librosa
  from magenta.models.onsets_frames_transcription import audio_transform
  from magenta.models.onsets_frames_transcription import constants
  from magenta.models.onsets_frames_transcription import drum_mappings
  from magenta.models.onsets_frames_transcription import melspec_input
  from note_seq import audio_io
  from note_seq import sequences_lib
  from note_seq.protobuf import music_pb2
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 27 for calling a global function with example usage: combine_tensor_batch(tensor, lengths, max_length, batch_size) and returns: combined_padded