class_docstr: Does Bayesian optimization with LCE-M GP.
class_docstr_tok: ["Does","Bayesian","optimization","with","LCE-M","GP","."]
code: def __init__(
      self,
      context_cat_feature: Optional[Tensor] = None,
      context_emb_feature: Optional[Tensor] = None,
      embs_dim_list: Optional[List[int]] = None,
  ) -> None:
      self.context_cat_feature = context_cat_feature
      self.context_emb_feature = context_emb_feature
      self.embs_dim_list = embs_dim_list
      super().__init__(model_constructor=self.get_and_fit_model)
  
file_path: Ax/ax/models/torch/cbo_lcem.py
imports: from typing import Any, Dict, List, Optional
  import torch
  from ax.models.torch.botorch import BotorchModel
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.contextual_multioutput import LCEMGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
  from torch import Tensor
label_desc: Use image node_id 1 to create a new LCEMBO object from inherited base classes: BotorchModel with example: obj = LCEMBO(context_cat_feature, context_emb_feature, embs_dim_list)