class_docstr: A Trainer that does nothing except read the data for a given number of epochs.
  
  It prints out as much debugging statistics as possible.
  
  This is useful for debugging data ingest problem. This trainer supports normal
  scaling options same as any other Trainer (e.g., num_workers, use_gpu).
  
  Args:
      scaling_config: Configuration for how to scale training. This is the same
          as for :class:`~ray.train.base_trainer.BaseTrainer`.
      num_epochs: How many many times to iterate through the datasets for.
      prefetch_batches: The number of batches to prefetch ahead of the
          current block during the scan. This is the same as
          :meth:`~ray.data.Dataset.iter_batches`
code: def __init__(
      self,
      *args,
      scaling_config: Optional[ScalingConfig] = None,
      num_epochs: int = 1,
      prefetch_batches: int = 1,
      batch_size: Optional[int] = 4096,
      # Deprecated.
      prefetch_blocks: int = 0,
      **kwargs,
  ):
      if not scaling_config:
          scaling_config = ScalingConfig(num_workers=1)
      super().__init__(
          train_loop_per_worker=DummyTrainer.make_train_loop(
              num_epochs, prefetch_batches, prefetch_blocks, batch_size
          ),
          *args,
          scaling_config=scaling_config,
          **kwargs,
      )
  
file_path: ray/python/ray/air/util/check_ingest.py
imports: import sys
  import time
  from typing import Optional
  import numpy
  import ray
  from ray import train
  from ray.air.config import DatasetConfig, ScalingConfig
  from ray.data import Dataset, DataIterator, Preprocessor
  from ray.train.data_parallel_trainer import DataParallelTrainer
  from ray.train import DataConfig
  from ray.util.annotations import Deprecated, DeveloperAPI
label_desc: Use image node_id 1 to create a new DummyTrainer object from inherited base classes: DataParallelTrainer with example: obj = DummyTrainer()