code: def test_double_precision_pad(self):
      import torch
  
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_tester.prepare_feat_extract_dict()
      )
      np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
      py_speech_inputs = np_speech_inputs.tolist()
  
      for inputs in [py_speech_inputs, np_speech_inputs]:
          np_processed = feature_extractor.pad(
              [{"input_features": inputs}], return_tensors="np"
          )
          self.assertTrue(
              np_processed.input_features.dtype == np.float32
          )
          pt_processed = feature_extractor.pad(
              [{"input_features": inputs}], return_tensors="pt"
          )
          self.assertTrue(
              pt_processed.input_features.dtype == torch.float32
          )
  
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 8 for calling the SeamlessM4TFeatureExtractionTest obj's underlying member method code with example usage: obj.test_double_precision_pad() without return types