code: def create_dataset(
      data_size: int,
      input_shape: List[int],
      num_classes: int,
      max_input_value: int = 1000,
  ) -> ds.Dataset:
      """Creates and returns a simple `Dataset` object for test."""
      features = tf.random.uniform(
          shape=[data_size] + input_shape,
          minval=0,
          maxval=max_input_value,
          dtype=tf.float32,
      )
  
      labels = tf.random.uniform(
          shape=[data_size],
          minval=0,
          maxval=num_classes,
          dtype=tf.int32,
      )
  
      tf_dataset = tf.data.Dataset.from_tensor_slices(
          (features, labels)
      )
      dataset = ds.Dataset(tf_dataset, data_size)
      return dataset
  
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 1 for calling a global function with example usage: create_dataset(data_size, input_shape, num_classes, max_input_value) and returns: dataset