code: def __init__(
      self,
      parent,
      batch_size=7,
      min_seq_length=400,
      max_seq_length=2000,
      feature_size=1,
      padding_value=0.0,
      sampling_rate=16000,
      do_normalize=True,
      num_mel_bins=80,
      hop_length=16,
      win_length=64,
      win_function="hann_window",
      fmin=80,
      fmax=7600,
      mel_floor=1e-10,
      return_attention_mask=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.padding_value = padding_value
      self.sampling_rate = sampling_rate
      self.do_normalize = do_normalize
      self.num_mel_bins = num_mel_bins
      self.hop_length = hop_length
      self.win_length = win_length
      self.win_function = win_function
      self.fmin = fmin
      self.fmax = fmax
      self.mel_floor = mel_floor
      self.return_attention_mask = return_attention_mask
  
imports: import itertools
  import random
  import unittest
  import numpy
  from transformers import BatchFeature, SpeechT5FeatureExtractor
  from transformers.testing_utils import 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 SpeechT5FeatureExtractionTester object from inherited base classes: unittest with example: obj = SpeechT5FeatureExtractionTester(parent, batch_size, min_seq_length, max_seq_length, feature_size, padding_value, sampling_rate, do_normalize, num_mel_bins, hop_length, win_length, win_function, fmin, fmax, mel_floor, return_attention_mask)