code: def _rf_predict(
      models: List[RandomForestRegressor], X: Tensor
  ) -> Tuple[Tensor, Tensor]:
      """Make predictions with Random Forest models.
  
      Args:
          models: List of models for each outcome
          X: X to predict
  
      Returns:
          mean and covariance estimates
      """
      f = np.zeros((X.shape[0], len(models)))
      cov = np.zeros((X.shape[0], len(models), len(models)))
      for i, m in enumerate(models):
          # pyre-fixme[16]: `RandomForestRegressor` has no attribute `estimators_`.
          preds = np.vstack(
              [tree.predict(X.numpy()) for tree in m.estimators_]
          )
          f[:, i] = preds.mean(0)
          cov[:, i, i] = preds.var(0)
      return torch.from_numpy(f), torch.from_numpy(cov)
  
imports: from __future__ import annotations
  from typing import Any, List, Optional, Tuple
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.core.types import TCandidateMetadata
  from ax.models.torch.utils import _datasets_to_legacy_inputs
  from ax.models.torch_base import TorchModel
  from ax.utils.common.docutils import copy_doc
  from botorch.utils.datasets import SupervisedDataset
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.tree import DecisionTreeRegressor
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: _rf_predict(models, X) and returns: torch,  torch