code: def build_synthetic_dataset(batch_size):
      image_dims = IMAGE_DIMS[1:]
      empty = np.empty(image_dims, dtype=np.uint8)
      ds = ray.data.from_items(
          [
              {"image": empty, "label": 1}
              for _ in range(int(batch_size))
          ],
          parallelism=1,
      )
      return ds
  
imports: from collections import defaultdict
  import tensorflow
  import numpy
  import os
  import pandas
  import time
  import logging
  import csv
  import json
  import torchvision
  import torch
  import ray
  from ray.train.tensorflow import prepare_dataset_shard, TensorflowTrainer
  from ray.train import DataConfig, ScalingConfig
  from ray import train, tune
  from ray.tune import Tuner
  from ray.data.datasource.partitioning import Partitioning
  from tf_utils import DEFAULT_IMAGE_SIZE, NUM_CHANNELS, preprocess_image, build_tf_dataset
  from metric_utils import determine_if_memory_monitor_is_enabled_in_latest_session, get_ray_spilled_and_restored_mb, MaxMemoryUtilizationTracker
label_desc: Use image node_id 7 for calling a global function with example usage: build_synthetic_dataset(batch_size) and returns: ds