class_docstr: A protocol that stands in for ``TorchModel`` like objects that
  have a ``predict`` method.
class_docstr_tok: ["A","protocol","that","stands","in","for","``","TorchModel","``","like","objects","that","have","a","``","predict","``","method","."]
docstr: Predicts outcomes given an input tensor.
  
  Args:
      X: A ``n x d`` tensor of input parameters.
  
  Returns:
      Tensor: The predicted posterior mean as an ``n x o``-dim tensor.
      Tensor: The predicted posterior covariance as a ``n x o x o``-dim tensor.
docstr_tok: ["Predicts","outcomes","given","an","input","tensor",".","Args",":","X",":","A","``","n","x","d","``","tensor","of","input","parameters",".","Returns",":","Tensor",":","The","predicted","posterior","mean","as","an","``","n","x","o","``","-dim","tensor",".","Tensor",":","The","predicted","posterior","covariance","as","a","``","n","x","o","x","o","``","-dim","tensor","."]
code: def predict(self, X: Tensor) -> Tuple[Tensor, Tensor]:
      """Predicts outcomes given an input tensor.
  
      Args:
          X: A ``n x d`` tensor of input parameters.
  
      Returns:
          Tensor: The predicted posterior mean as an ``n x o``-dim tensor.
          Tensor: The predicted posterior covariance as a ``n x o x o``-dim tensor.
      """
      ...
  
imports: from __future__ import annotations
  import itertools
  import warnings
  from typing import Callable, Dict, List, Optional, Protocol, Set, Tuple, Union
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import SearchSpaceExhausted, UnsupportedError
  from ax.models.types import TConfig
  from ax.utils.common.typeutils import checked_cast
  from botorch.acquisition.risk_measures import RiskMeasureMCObjective
  from torch import Tensor
label_desc: Use image node_id 1 for calling the TorchModelLike obj's underlying member method code with example usage: obj.predict(X) without return types