class_docstr: Iterable Dataset with output ports
  
  Please Note: Subclasses of IterableDataset should *not* implement input_types.
class_docstr_tok: ["Iterable","Dataset","with","output","ports","Please","Note",":","Subclasses","of","IterableDataset","should","*","not","*","implement","input_types","."]
docstr: This is the method that user pass as functor to DataLoader.
  The method optionally performs neural type checking and add types to the outputs.
  
  # Usage:
  dataloader = torch.utils.data.DataLoader(
          ....,
          collate_fn=dataset.collate_fn,
          ....
  )
  
  Returns:
      Collated batch, with or without types.
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code: def collate_fn(self, batch):
      """
      This is the method that user pass as functor to DataLoader.
      The method optionally performs neural type checking and add types to the outputs.
  
      # Usage:
      dataloader = torch.utils.data.DataLoader(
              ....,
              collate_fn=dataset.collate_fn,
              ....
      )
  
      Returns:
          Collated batch, with or without types.
      """
      if self.input_types is not None:
          raise TypeError(
              "Datasets should not implement `input_types` as they are not checked"
          )
  
      # Simply forward the inner `_collate_fn`
      return self._collate_fn(batch)
  
file_path: NeMo/nemo/core/classes/dataset.py
imports: from dataclasses import dataclass
  from typing import Optional
  from torch.utils import data
  from nemo.core.classes import Serialization, Typing, typecheck
label_desc: Use image node_id 2 for calling the IterableDataset obj's underlying member method code with example usage: obj.collate_fn(batch) and returns: self