code: def __init__(
      self,
      parent,
      batch_size=7,
      min_seq_length=400,
      max_seq_length=2000,
      feature_size=24,
      num_mel_bins=24,
      padding_value=0.0,
      sampling_rate=16_000,
      return_attention_mask=True,
      do_normalize=True,
  ):
      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.feature_size = feature_size
      self.num_mel_bins = num_mel_bins
      self.padding_value = padding_value
      self.sampling_rate = sampling_rate
      self.return_attention_mask = return_attention_mask
      self.do_normalize = do_normalize
  
imports: import itertools
  import random
  import unittest
  import numpy
  from transformers import is_speech_available
  from transformers.testing_utils import require_torch, require_torchaudio
  from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
label_desc: Use image node_id 1 to create a new Speech2TextFeatureExtractionTester object from inherited base classes: unittest with example: obj = Speech2TextFeatureExtractionTester(parent, batch_size, min_seq_length, max_seq_length, feature_size, num_mel_bins, padding_value, sampling_rate, return_attention_mask, do_normalize)