class_docstr: This class specifies the interface for a torch-based model.
  
  These methods should be implemented to have access to all of the features
  of Ax.
docstr: Evaluate the acquisition function on the candidate set `X`.
  
  Args:
      X: (j x d) tensor of the j points at which to evaluate the acquisition
          function.
      search_space_digest: A dataclass used to compactly represent a search space.
      torch_opt_config: A TorchOptConfig object containing optimization
          arguments (e.g., objective weights, constraints).
      acq_options: Keyword arguments used to contruct the acquisition function.
  
  Returns:
      A single-element tensor with the acquisition value for these points.
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code: def evaluate_acquisition_function(
      self,
      X: Tensor,
      search_space_digest: SearchSpaceDigest,
      torch_opt_config: TorchOptConfig,
      acq_options: Optional[Dict[str, Any]] = None,
  ) -> Tensor:
      """Evaluate the acquisition function on the candidate set `X`.
  
      Args:
          X: (j x d) tensor of the j points at which to evaluate the acquisition
              function.
          search_space_digest: A dataclass used to compactly represent a search space.
          torch_opt_config: A TorchOptConfig object containing optimization
              arguments (e.g., objective weights, constraints).
          acq_options: Keyword arguments used to contruct the acquisition function.
  
      Returns:
          A single-element tensor with the acquisition value for these points.
      """
      raise NotImplementedError
  
imports: from __future__ import annotations
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from ax.core.metric import Metric
  from ax.core.search_space import SearchSpaceDigest
  from ax.core.types import TCandidateMetadata
  from ax.models.base import Model
  from ax.models.types import TConfig
  from botorch.acquisition.risk_measures import RiskMeasureMCObjective
  from botorch.utils.datasets import SupervisedDataset
  from torch import Tensor
label_desc: Use image node_id 7 for calling the TorchModel obj's underlying member method code with example usage: obj.evaluate_acquisition_function(X, search_space_digest, torch_opt_config, acq_options) without return types