class_docstr: A list of box decompositions.
class_docstr_tok: ["A","list","of","box","decompositions","."]
docstr: Update the partitioning.
  
  Args:
      Y: A `n_box_decompositions x n x num_outcomes`-dim tensor or a list
          where the ith  element contains the new points for
          box_decomposition `i`.
docstr_tok: ["Update","the","partitioning",".","Args",":","Y",":","A","`","n_box_decompositions","x","n","x","num_outcomes","`","-dim","tensor","or","a","list","where","the","ith","element","contains","the","new","points","for","box_decomposition","`","i","`","."]
code: def update(self, Y: Union[List[Tensor], Tensor]) -> None:
      r"""Update the partitioning.
  
      Args:
          Y: A `n_box_decompositions x n x num_outcomes`-dim tensor or a list
              where the ith  element contains the new points for
              box_decomposition `i`.
      """
      if (
          torch.is_tensor(Y)
          and Y.ndim != 3
          and Y.shape[0] != len(self.box_decompositions)
      ) or (
          isinstance(Y, List) and len(Y) != len(self.box_decompositions)
      ):
          raise BotorchTensorDimensionError(
              "BoxDecompositionList.update requires either a batched tensor Y, "
              "with one batch per box decomposition or a list of tensors with "
              "one element per box decomposition."
          )
      for i, p in enumerate(self.box_decompositions):
          p.update(Y[i])
  
file_path: botorch/botorch/utils/multi_objective/box_decompositions/box_decomposition_list.py
imports: from __future__ import annotations
  from typing import List, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.utils.multi_objective.box_decompositions.box_decomposition import BoxDecomposition
  from torch import Tensor
  from torch.nn import Module, ModuleList
label_desc: Use image node_id 5 for calling the BoxDecompositionList obj's underlying member method code with example usage: obj.update(Y) without return types