class_docstr: A generic dataset for iterable data source and an optional callable data transform
  when fetching a data sample. Inherit from PyTorch IterableDataset:
  https://pytorch.org/docs/stable/data.html?highlight=iterabledataset#torch.utils.data.IterableDataset.
  For example, typical input data can be web data stream which can support multi-process access.
  
  To accelerate the loading process, it can support multi-processing based on PyTorch DataLoader workers,
  every process executes transforms on part of every loaded data.
  Note that the order of output data may not match data source in multi-processing mode.
  And each worker process will have a different copy of the dataset object, need to guarantee
  process-safe from data source or DataLoader.
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docstr: Args:
      data: input data source to load and transform to generate dataset for model.
      transform: a callable data transform on input data.
docstr_tok: ["Args",":","data",":","input","data","source","to","load","and","transform","to","generate","dataset","for","model",".","transform",":","a","callable","data","transform","on","input","data","."]
code: def __init__(
      self, data: Iterable[Any], transform: Callable | None = None
  ) -> None:
      """
      Args:
          data: input data source to load and transform to generate dataset for model.
          transform: a callable data transform on input data.
      """
      self.data = data
      self.transform = transform
      self.source: Iterator[Any] | None = None
  
imports: from __future__ import annotations
  from collections.abc import Callable, Iterable, Iterator, Sequence
  from typing import Any
  from torch.utils.data import IterableDataset
  from torch.utils.data import get_worker_info
  from monai.data.utils import convert_tables_to_dicts
  from monai.transforms import apply_transform
  from monai.transforms.transform import Randomizable
  from monai.utils import optional_import
label_desc: Use image node_id 1 to create a new IterableDataset object from inherited base classes: _TorchIterableDataset with example: obj = IterableDataset(data, transform)