code: def __init__(
      self,
      parent,
      batch_size=7,
      min_seq_length=400,
      max_seq_length=2000,
      feature_size=10,
      padding_value=0.0,
      sampling_rate=4_000,
      return_attention_mask=True,
      do_normalize=True,
      stride=2,
  ):
      self.parent = parent
      self.batch_size = batch_size
      self.min_seq_length = min_seq_length
      self.max_seq_length = max_seq_length
      self.seq_length_diff = (
          self.max_seq_length - self.min_seq_length
      ) // (self.batch_size - 1)
      self.padding_value = padding_value
      self.sampling_rate = sampling_rate
      self.return_attention_mask = return_attention_mask
      self.do_normalize = do_normalize
      self.feature_size = feature_size
      self.stride = stride
      self.num_mel_bins = feature_size
  
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 1 to create a new SeamlessM4TFeatureExtractionTester object from inherited base classes: unittest with example: obj = SeamlessM4TFeatureExtractionTester(parent, batch_size, min_seq_length, max_seq_length, feature_size, padding_value, sampling_rate, return_attention_mask, do_normalize, stride)