class_docstr: A generic transformation of a posterior (implicitly represented).
class_docstr_tok: ["A","generic","transformation","of","a","posterior","(","implicitly","represented",")","."]
docstr: Returns the shape of the samples produced by the posterior with
  the given `sample_shape`.
  
  NOTE: This assumes that the `sample_transform` does not change the
  shape of the samples.
docstr_tok: ["Returns","the","shape","of","the","samples","produced","by","the","posterior","with","the","given","`","sample_shape","`",".","NOTE",":","This","assumes","that","the","`","sample_transform","`","does","not","change","the","shape","of","the","samples","."]
code: def _extended_shape(
      self, sample_shape: torch.Size = torch.Size()  # noqa: B008
  ) -> torch.Size:
      r"""Returns the shape of the samples produced by the posterior with
      the given `sample_shape`.
  
      NOTE: This assumes that the `sample_transform` does not change the
      shape of the samples.
      """
      return self._posterior._extended_shape(sample_shape=sample_shape)
  
file_path: botorch/botorch/posteriors/transformed.py
imports: from __future__ import annotations
  from typing import Callable, Optional, Tuple
  import torch
  from botorch.posteriors.posterior import Posterior
  from torch import Tensor
label_desc: Use image node_id 6 for calling the TransformedPosterior obj's underlying member method code with example usage: obj._extended_shape(sample_shape) and returns: self