class_docstr: Create a naive dataset for the benchmark
class_docstr_tok: ["Create","a","naive","dataset","for","the","benchmark"]
code: def __init__(self, dim, size=1000):
      self.x = torch.from_numpy(
          np.random.normal(size=(size, dim))
      ).float()
      self.y = torch.from_numpy(
          np.random.normal(size=(size, 1))
      ).float()
      self.size = size
  
file_path: ray/python/ray/train/examples/pytorch/torch_data_prefetch_benchmark/auto_pipeline_for_host_to_device_data_transfer.py
imports: import argparse
  import warnings
  import numpy
  import torch
  import torch.nn
  import ray.train
  from ray.train import ScalingConfig
  from ray.train.torch import TorchTrainer
label_desc: Use image node_id 1 to create a new BenchmarkDataset object from inherited base classes: torch.utils.data with example: obj = BenchmarkDataset(dim, size)