class_docstr: A multi-output GP model with independent GPs for the outputs.
  
  This model supports different-shaped training inputs for each of its
  sub-models. It can be used with any number of single-output
  `GPyTorchModel`\s and the models can be of different types. Use this model
  when you have independent outputs with different training data. When
  modeling correlations between outputs, use `MultiTaskGP`.
  
  Internally, this model is just a list of individual models, but it implements
  the same input/output interface as all other BoTorch models. This makes it
  very flexible and convenient to work with. The sequential evaluation comes
  at a performance cost though - if you are using a block design (i.e. the
  same number of training example for each output, and a similar model
  structure, you should consider using a batched GP model instead, such as
  `SingleTaskGP` with batched inputs).
class_docstr_tok: ["A","multi-output","GP","model","with","independent","GPs","for","the","outputs",".","This","model","supports","different-shaped","training","inputs","for","each","of","its","sub-models",".","It","can","be","used","with","any","number","of","single-output","`","GPyTorchModel","`","\\s","and","the","models","can","be","of","different","types",".","Use","this","model","when","you","have","independent","outputs","with","different","training","data",".","When","modeling","correlations","between","outputs",",","use","`","MultiTaskGP","`",".","Internally",",","this","model","is","just","a","list","of","individual","models",",","but","it","implements","the","same","input\/output","interface","as","all","other","BoTorch","models",".","This","makes","it","very","flexible","and","convenient","to","work","with",".","The","sequential","evaluation","comes","at","a","performance","cost","though","-","if","you","are","using","a","block","design","(","i.e",".","the","same","number","of","training","example","for","each","output",",","and","a","similar","model","structure",",","you","should","consider","using","a","batched","GP","model","instead",",","such","as","`","SingleTaskGP","`","with","batched","inputs",")","."]
docstr: Update training inputs with transformed inputs.
docstr_tok: ["Update","training","inputs","with","transformed","inputs","."]
code: def _set_transformed_inputs(self) -> None:
      r"""Update training inputs with transformed inputs."""
      for m in self.models:
          m._set_transformed_inputs()
  
file_path: botorch/botorch/models/model_list_gp_regression.py
imports: from __future__ import annotations
  from copy import deepcopy
  from typing import Any, List
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.models.gpytorch import GPyTorchModel, ModelListGPyTorchModel
  from botorch.models.model import FantasizeMixin
  from gpytorch.models import IndependentModelList
  from torch import Tensor
label_desc: Use image node_id 4 for calling the ModelListGP obj's underlying member method code with example usage: obj._set_transformed_inputs() without return types