class_docstr: An algorithm for approximating Gaussian probabilities `P(X \in bounds)`, where
  `X ~ N(0, covariance_matrix)`.
class_docstr_tok: ["An","algorithm","for","approximating","Gaussian","probabilities","`","P","(","X","\\in","bounds",")","`",",","where","`","X","~","N","(","0",",","covariance_matrix",")","`","."]
code: def __getitem__(self, key: Any) -> MVNXPB:
      return self.build(
          step=self.step,
          perm=self.perm[key],
          bounds=self.bounds[key],
          piv_chol=self.piv_chol[key],
          plug_ins=self.plug_ins[key],
          log_prob=self.log_prob[key],
          log_prob_extra=(
              None
              if self.log_prob_extra is None
              else self.log_prob_extra[key]
          ),
      )
  
imports: from __future__ import annotations
  from typing import Any, Optional, TypedDict
  from warnings import warn
  import torch
  from botorch.utils.probability.bvn import bvn, bvnmom
  from botorch.utils.probability.linalg import augment_cholesky, block_matrix_concat, PivotedCholesky
  from botorch.utils.probability.utils import case_dispatcher, get_constants_like, ndtr, phi, STANDARDIZED_RANGE, swap_along_dim_
  from botorch.utils.safe_math import log, mul
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from linear_operator.utils.errors import NotPSDError
  from torch import LongTensor, Tensor
  from torch.nn.functional import pad
label_desc: Use image node_id 6 for calling the MVNXPB obj's underlying member method code with example usage: obj.__getitem__(key) and returns: self