code: def prepare_inputs_for_common(
      self, equal_length=False, numpify=False
  ):
      def _flatten(list_of_lists):
          return list(itertools.chain(*list_of_lists))
  
      if equal_length:
          speech_inputs = [
              floats_list((self.max_seq_length, self.feature_size))
              for _ in range(self.batch_size)
          ]
      else:
          # make sure that inputs increase in size
          speech_inputs = [
              floats_list((x, self.feature_size))
              for x in range(
                  self.min_seq_length,
                  self.max_seq_length,
                  self.seq_length_diff,
              )
          ]
      if numpify:
          speech_inputs = [np.asarray(x) for x in speech_inputs]
      return speech_inputs
  
imports: import itertools
  import os
  import random
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from transformers import SeamlessM4TFeatureExtractor, is_speech_available
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch
  from transformers.utils.import_utils import is_torch_available
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 3 for calling the SeamlessM4TFeatureExtractionTester obj's underlying member method code with example usage: obj.prepare_inputs_for_common(equal_length, numpify) and returns: speech_inputs, list