class_docstr: Abstract base class for cost-aware utilities.
  
  :meta private:
class_docstr_tok: ["Abstract","base","class","for","cost-aware","utilities",".",":","meta","private",":"]
docstr: Evaluate the cost-aware utility on the candidates and improvements.
  
  Args:
      X: A `batch_shape x q x d`-dim Tensor of with `q` `d`-dim design
          points each for each t-batch.
      deltas: A `num_fantasies x batch_shape`-dim Tensor of `num_fantasy`
          samples from the marginal improvement in utility over the
          current state at `X` for each t-batch.
  
  Returns:
      A `num_fantasies x batch_shape`-dim Tensor of cost-transformed utilities.
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code: def forward(self, X: Tensor, deltas: Tensor, **kwargs: Any) -> Tensor:
      r"""Evaluate the cost-aware utility on the candidates and improvements.
  
      Args:
          X: A `batch_shape x q x d`-dim Tensor of with `q` `d`-dim design
              points each for each t-batch.
          deltas: A `num_fantasies x batch_shape`-dim Tensor of `num_fantasy`
              samples from the marginal improvement in utility over the
              current state at `X` for each t-batch.
  
      Returns:
          A `num_fantasies x batch_shape`-dim Tensor of cost-transformed utilities.
      """
      pass
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Optional, Union
  import torch
  from botorch import settings
  from botorch.acquisition.objective import GenericMCObjective, IdentityMCObjective, MCAcquisitionObjective
  from botorch.exceptions.warnings import CostAwareWarning
  from botorch.models.deterministic import DeterministicModel
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.sampling.base import MCSampler
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 for calling the CostAwareUtility obj's underlying member method code with example usage: obj.forward(X, deltas) without return types