code: def create_random_sample(
      size: Union[int, List[int]], low: float = 0, high: float = 1
  ) -> np.ndarray:
      """Creates and returns a random sample with floating point values.
  
      Args:
        size: Size of the output multi-dimensional array.
        low: Lower boundary of the output values.
        high: Higher boundary of the output values.
  
      Returns:
        1D array if the size is scalar. Otherwise, N-D array whose dimension equals
        input size.
      """
      np.random.seed(0)
      return np.random.uniform(low=low, high=high, size=size).astype(
          np.float32
      )
  
file_path: mediapipe/mediapipe/model_maker/python/core/utils/test_util.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Sequence
  from typing import Dict, List, Union
  import numpy
  import tensorflow
  from mediapipe.model_maker.python.core.data import dataset
  from mediapipe.model_maker.python.core.utils import model_util
label_desc: Use image node_id 2 for calling a global function with example usage: create_random_sample(size, low, high) and returns: np