class_docstr: Abstract base class for datasets. Subclasses should implement the `__getitem__` and `__len__` methods.
class_docstr_tok: ["Abstract","base","class","for","datasets",".","Subclasses","should","implement","the","`","__getitem__","`","and","`","__len__","`","methods","."]
docstr: Used by DataLoader to group a list of individual samples into a batch.
  By default tries to stack elements in the samples according to their positiion.
  Can be overridden for more complex use cases.
docstr_tok: ["Used","by","DataLoader","to","group","a","list","of","individual","samples","into","a","batch",".","By","default","tries","to","stack","elements","in","the","samples","according","to","their","positiion",".","Can","be","overridden","for","more","complex","use","cases","."]
code: def batch_fn(
      self, list_of_samples: tp.List[tp.Any]
  ) -> tp.Union[jnp.ndarray, tp.Tuple[jnp.ndarray]]:
      """Used by DataLoader to group a list of individual samples into a batch.
      By default tries to stack elements in the samples according to their positiion.
      Can be overridden for more complex use cases.
      """
      return default_batch_fn(list_of_samples)
  
imports: import multiprocessing.pool
  import os
  import typing
  import jax
  import jax.numpy
  import numpy
  from .data_adapter import DataAdapter
  from .utils import is_none_or_empty
label_desc: Use image node_id 3 for calling the Dataset obj's underlying member method code with example usage: obj.batch_fn(list_of_samples) and returns: default_batch_fn