code: def floats_list(shape, scale=1.0, rng=None, name=None):
      """Creates a random float32 tensor"""
      if rng is None:
          rng = global_rng
  
      values = []
      for batch_idx in range(shape[0]):
          values.append([])
          for _ in range(shape[1]):
              values[-1].append(rng.random() * scale)
  
      return values
  
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 for calling a global function with example usage: floats_list(shape, scale, rng, name) and returns: values