class_docstr: Abstract base class for botorch posteriors.
  
  :meta private:
class_docstr_tok: ["Abstract","base","class","for","botorch","posteriors",".",":","meta","private",":"]
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.
      """
      raise NotImplementedError(
          f"{self.__class__.__name__} does not implement `batch_range`."
      )
  
file_path: botorch/botorch/posteriors/posterior.py
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod, abstractproperty
  from typing import Optional, Tuple
  import torch
  from torch import Tensor
label_desc: Use image node_id 11 for calling the Posterior obj's underlying member method code with example usage: obj.batch_range() without return types