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: Fit model to m outcomes.
  
  Args:
      datasets: A list of ``SupervisedDataset`` containers, each
          corresponding to the data of one metric (outcome).
      metric_names: A list of metric names, with the i-th metric
          corresponding to the i-th dataset.
      search_space_digest: A ``SearchSpaceDigest`` object containing
          metadata on the features in the datasets.
      candidate_metadata: Model-produced metadata for candidates, in
          the order corresponding to the Xs.
docstr_tok: ["Fit","model","to","m","outcomes",".","Args",":","datasets",":","A","list","of","``","SupervisedDataset","``","containers",",","each","corresponding","to","the","data","of","one","metric","(","outcome",")",".","metric_names",":","A","list","of","metric","names",",","with","the","i-th","metric","corresponding","to","the","i-th","dataset",".","search_space_digest",":","A","``","SearchSpaceDigest","``","object","containing","metadata","on","the","features","in","the","datasets",".","candidate_metadata",":","Model-produced","metadata","for","candidates",",","in","the","order","corresponding","to","the","Xs","."]
code: def fit(
      self,
      datasets: List[SupervisedDataset],
      metric_names: List[str],
      search_space_digest: SearchSpaceDigest,
      candidate_metadata: Optional[
          List[List[TCandidateMetadata]]
      ] = None,
  ) -> None:
      """Fit model to m outcomes.
  
      Args:
          datasets: A list of ``SupervisedDataset`` containers, each
              corresponding to the data of one metric (outcome).
          metric_names: A list of metric names, with the i-th metric
              corresponding to the i-th dataset.
          search_space_digest: A ``SearchSpaceDigest`` object containing
              metadata on the features in the datasets.
          candidate_metadata: Model-produced metadata for candidates, in
              the order corresponding to the Xs.
      """
      pass
  
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 1 for calling the TorchModel obj's underlying member method code with example usage: obj.fit(datasets, metric_names, search_space_digest, candidate_metadata) without return types