class_docstr: A list of SamplePaths.
docstr: Initializes a PathList instance.
  
  Args:
      paths: An optional iterable of sample paths.
      join: An optional callable used to combine each path's outputs.
      input_transform: An optional input transform for the module.
      output_transform: An optional output transform for the module.
docstr_tok: ["Initializes","a","PathList","instance",".","Args",":","paths",":","An","optional","iterable","of","sample","paths",".","join",":","An","optional","callable","used","to","combine","each","path","'s","outputs",".","input_transform",":","An","optional","input","transform","for","the","module",".","output_transform",":","An","optional","output","transform","for","the","module","."]
code: def __init__(
      self,
      paths: Optional[Iterable[SamplePath]] = None,
      join: Optional[Callable[[List[Tensor]], Tensor]] = None,
      input_transform: Optional[TInputTransform] = None,
      output_transform: Optional[TOutputTransform] = None,
  ) -> None:
      r"""Initializes a PathList instance.
  
      Args:
          paths: An optional iterable of sample paths.
          join: An optional callable used to combine each path's outputs.
          input_transform: An optional input transform for the module.
          output_transform: An optional output transform for the module.
      """
  
      if join is None and output_transform is not None:
          raise UnsupportedError(
              "Output transforms must be preceded by a join rule."
          )
  
      super().__init__()
      self.join = join
      self.input_transform = input_transform
      self.output_transform = output_transform
      self.paths = (
          paths
          if isinstance(paths, ModuleList)
          else ModuleList({} if paths is None else paths)
      )
  
imports: from __future__ import annotations
  from abc import ABC
  from typing import Any, Callable, Dict, Iterable, Iterator, List, Mapping, Optional, Tuple, Union
  from botorch.exceptions.errors import UnsupportedError
  from botorch.sampling.pathwise.features import FeatureMap
  from botorch.sampling.pathwise.utils import TInputTransform, TOutputTransform, TransformedModuleMixin
  from torch import Tensor
  from torch.nn import Module, ModuleDict, ModuleList, Parameter
label_desc: Use image node_id 1 to create a new PathList object from inherited base classes: SamplePath with example: obj = PathList(paths, join, input_transform, output_transform)