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 concat(self, other: MVNXPB, dim: int) -> MVNXPB:
      if not isinstance(other, MVNXPB):
          raise TypeError(
              f"Expected `other` to be {type(self)} typed but was {type(other)}."
          )
  
      batch_ndim = self.log_prob.ndim
      if dim > batch_ndim or dim < -batch_ndim:
          raise ValueError(
              f"`dim={dim}` is not a valid batch dimension."
          )
  
      state_dict = self.asdict()
      for key, _other in other.asdict().items():
          _self = state_dict.get(key)
          if _self is None and _other is None:
              continue
  
          if type(_self) is not type(_other):
              raise TypeError(
                  f"Concatenation failed: `self.{key}` has type {type(_self)}, "
                  f"but `other.{key}` is of type {type(_self)}."
              )
  
          if isinstance(_self, PivotedCholesky):
              state_dict[key] = _self.concat(_other, dim=dim)
          elif isinstance(_self, Tensor):
              state_dict[key] = torch.concat((_self, _other), dim=dim)
          elif _self != _other:
              raise ValueError(
                  f"Concatenation failed: `self.{key}` does not equal `other.{key}`."
              )
  
      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 7 for calling the MVNXPB obj's underlying member method code with example usage: obj.concat(other, dim) and returns: self