code: def get_deterministic_model_list(
      weights: List[Tensor],
      bases: List[RandomFourierFeatures],
  ) -> ModelList:
      """Get a deterministic model list using the provided weights and bases
      for each output.
  
      Args:
          weights: A list of weights with `m` elements.
          bases: A list of `RandomFourierFeatures` with `m` elements.
  
      Returns:
          A deterministic model.
      """
      samples = []
      for w, basis in zip(weights, bases):
          sample = GenericDeterministicModel(
              f=get_eval_gp_sample_callable(w=w, basis=basis),
              num_outputs=1,
          )
          samples.append(sample)
      return ModelList(*samples)
  
imports: from __future__ import annotations
  from copy import deepcopy
  from math import pi
  from typing import List, Optional
  import torch
  from botorch.models.converter import batched_to_model_list
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.model import Model, ModelList
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.utils.sampling import manual_seed
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import Kernel, MaternKernel, RBFKernel, ScaleKernel
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from torch import Tensor
  from torch.distributions import MultivariateNormal
  from torch.nn import Module
label_desc: Use image node_id 4 for calling a global function with example usage: get_deterministic_model_list(weights, bases) and returns: ModelList