class_docstr: A generic transformation of a posterior (implicitly represented).
class_docstr_tok: ["A","generic","transformation","of","a","posterior","(","implicitly","represented",")","."]
docstr: The t-batch range.
  
  This is used in samplers to identify the t-batch component of the
  `base_sample_shape`. The base samples are expanded over the t-batches to
  provide consistency in the acquisition values, i.e., to ensure that a
  candidate produces same value regardless of its position on the t-batch.
docstr_tok: ["The","t-batch","range",".","This","is","used","in","samplers","to","identify","the","t-batch","component","of","the","`","base_sample_shape","`",".","The","base","samples","are","expanded","over","the","t-batches","to","provide","consistency","in","the","acquisition","values",",","i.e.",",","to","ensure","that","a","candidate","produces","same","value","regardless","of","its","position","on","the","t-batch","."]
code: def batch_range(self) -> Tuple[int, int]:
      r"""The t-batch range.
  
      This is used in samplers to identify the t-batch component of the
      `base_sample_shape`. The base samples are expanded over the t-batches to
      provide consistency in the acquisition values, i.e., to ensure that a
      candidate produces same value regardless of its position on the t-batch.
      """
      return self._posterior.batch_range
  
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 3 for calling the TransformedPosterior obj's underlying member method code with example usage: obj.batch_range() and returns: self