code: def __init__(
      self,
      root,
      reader=None,
      split="train",
      class_map=None,
      is_training=False,
      batch_size=1,
      num_samples=None,
      seed=42,
      repeats=0,
      download=False,
      input_img_mode="RGB",
      input_key=None,
      target_key=None,
      transform=None,
      target_transform=None,
      max_steps=None,
  ):
      assert reader is not None
      if isinstance(reader, str):
          self.reader = create_reader(
              reader,
              root=root,
              split=split,
              class_map=class_map,
              is_training=is_training,
              batch_size=batch_size,
              num_samples=num_samples,
              seed=seed,
              repeats=repeats,
              download=download,
              input_img_mode=input_img_mode,
              input_key=input_key,
              target_key=target_key,
              max_steps=max_steps,
          )
      else:
          self.reader = reader
      self.transform = transform
      self.target_transform = target_transform
      self._consecutive_errors = 0
  
imports: import io
  import logging
  from typing import Optional
  import torch
  import torch.utils.data
  from PIL import Image
  from .readers import create_reader
label_desc: Use image node_id 1 to create a new IterableImageDataset object from inherited base classes: data with example: obj = IterableImageDataset(root, reader, split, class_map, is_training, batch_size, num_samples, seed, repeats, download, input_img_mode, input_key, target_key, transform, target_transform, max_steps)