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 expand(self, *sizes: int) -> MVNXPB:
      state_dict = self.asdict()
      state_dict["piv_chol"] = state_dict["piv_chol"].expand(*sizes)
      for name, ndim in {
          "bounds": 2,
          "perm": 1,
          "plug_ins": 1,
          "log_prob": 0,
          "log_prob_extra": 0,
      }.items():
          src = state_dict[name]
          if isinstance(src, Tensor):
              state_dict[name] = src.expand(
                  sizes + src.shape[-ndim:] if ndim else sizes
              )
      return self.build(**state_dict)
  
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 8 for calling the MVNXPB obj's underlying member method code with example usage: obj.expand() and returns: self