class_docstr: Trivial objective that returns the unaltered samples.
  
  Example:
      >>> identity_objective = IdentityMCMultiOutputObjective()
      >>> samples = sampler(posterior)
      >>> objective = identity_objective(samples)
docstr: Initialize Objective.
  
  Args:
      outcomes: A list of the `m'` indices that the weights should be
          applied to.
      num_outcomes: The total number of outcomes `m`
docstr_tok: ["Initialize","Objective",".","Args",":","outcomes",":","A","list","of","the","`","m","'","`","indices","that","the","weights","should","be","applied","to",".","num_outcomes",":","The","total","number","of","outcomes","`","m","`"]
code: def __init__(
      self,
      outcomes: Optional[List[int]] = None,
      num_outcomes: Optional[int] = None,
  ) -> None:
      r"""Initialize Objective.
  
      Args:
          outcomes: A list of the `m'` indices that the weights should be
              applied to.
          num_outcomes: The total number of outcomes `m`
      """
      super().__init__()
      if outcomes is not None:
          if len(outcomes) < 2:
              raise BotorchTensorDimensionError(
                  "Must specify at least two outcomes for MOO."
              )
          if any(i < 0 for i in outcomes):
              if num_outcomes is None:
                  raise BotorchError(
                      "num_outcomes is required if any outcomes are less than 0."
                  )
              outcomes = normalize_indices(outcomes, num_outcomes)
          self.register_buffer(
              "outcomes", torch.tensor(outcomes, dtype=torch.long)
          )
  
imports: from __future__ import annotations
  import warnings
  from abc import abstractmethod
  from typing import List, Optional
  import torch
  from botorch.acquisition.objective import GenericMCObjective, MCAcquisitionObjective
  from botorch.exceptions.errors import BotorchError, BotorchTensorDimensionError
  from botorch.models.model import Model
  from botorch.posteriors import GPyTorchPosterior
  from botorch.utils import apply_constraints
  from botorch.utils.transforms import normalize_indices
  from torch import Tensor
label_desc: Use image node_id 1 to create a new IdentityMCMultiOutputObjective object from inherited base classes: MCMultiOutputObjective with example: obj = IdentityMCMultiOutputObjective(outcomes, num_outcomes)