code: def get_deterministic_model(
      weights: List[Tensor], bases: List[RandomFourierFeatures]
  ) -> GenericDeterministicModel:
      """Get a deterministic model 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.
      """
      callables = [
          get_eval_gp_sample_callable(w=w, basis=basis)
          for w, basis in zip(weights, bases)
      ]
  
      def evaluate_gp_sample(X):
          return torch.cat([c(X) for c in callables], dim=-1)
  
      return GenericDeterministicModel(
          f=evaluate_gp_sample, num_outputs=len(weights)
      )
  
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 3 for calling a global function with example usage: get_deterministic_model(weights, bases) and returns: GenericDeterministicModel, torch