docstr: Returns a new GP model that incorporates the specified inputs and targets as new training data.
  
  This is a simple wrapper that creates fantasy models for each of the models in the model list,
  and returns the same class of fantasy models.
  
  Args:
      inputs: List of locations of fantasy observations, one for each model.
      targets List of labels of fantasy observations, one for each model.
  
  Returns:
      An `IndependentModelList` model, where each sub-model is the fantasy model of the respective
      sub-model in the original model at the corresponding input locations / labels.
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code: def get_fantasy_model(self, inputs, targets, **kwargs):
      """
      Returns a new GP model that incorporates the specified inputs and targets as new training data.
  
      This is a simple wrapper that creates fantasy models for each of the models in the model list,
      and returns the same class of fantasy models.
  
      Args:
          inputs: List of locations of fantasy observations, one for each model.
          targets List of labels of fantasy observations, one for each model.
  
      Returns:
          An `IndependentModelList` model, where each sub-model is the fantasy model of the respective
          sub-model in the original model at the corresponding input locations / labels.
      """
  
      if "noise" in kwargs:
          noise = kwargs.pop("noise")
          kwargs = [
              {**kwargs, "noise": noise_}
              if noise_ is not None
              else kwargs
              for noise_ in noise
          ]
      else:
          kwargs = [kwargs] * len(inputs)
  
      fantasy_models = [
          model.get_fantasy_model(*inputs_, *targets_, **kwargs_)
          for model, inputs_, targets_, kwargs_ in length_safe_zip(
              self.models,
              _get_tensor_args(*inputs),
              _get_tensor_args(*targets),
              kwargs,
          )
      ]
      return self.__class__(*fantasy_models)
  
imports: from abc import ABC
  import torch
  from torch.nn import ModuleList
  from gpytorch.likelihoods import LikelihoodList
  from gpytorch.models import GP
  from gpytorch.utils.generic import length_safe_zip
label_desc: Use image node_id 5 for calling the IndependentModelList obj's underlying member method code with example usage: obj.get_fantasy_model(inputs, targets) and returns: self