class_docstr: BaseTrainerModel model expanding TrainerModel with required functions by 🐸TTS.
  
  Every new 🐸TTS model must inherit it.
docstr: Forward pass for inference.
  
  It must return a dictionary with the main model output and all the auxiliary outputs. The key ```model_outputs```
  is considered to be the main output and you can add any other auxiliary outputs as you want.
  
  We don't use `*kwargs` since it is problematic with the TorchScript API.
  
  Args:
      input (torch.Tensor): [description]
      aux_input (Dict): Auxiliary inputs like speaker embeddings, durations etc.
  
  Returns:
      Dict: [description]
code: def inference(self, input: torch.Tensor, aux_input={}) -> Dict:
      """Forward pass for inference.
  
      It must return a dictionary with the main model output and all the auxiliary outputs. The key ```model_outputs```
      is considered to be the main output and you can add any other auxiliary outputs as you want.
  
      We don't use `*kwargs` since it is problematic with the TorchScript API.
  
      Args:
          input (torch.Tensor): [description]
          aux_input (Dict): Auxiliary inputs like speaker embeddings, durations etc.
  
      Returns:
          Dict: [description]
      """
      outputs_dict = {"model_outputs": None}
      ...
      return outputs_dict
  
file_path: TTS/TTS/model.py
imports: from abc import abstractmethod
  from typing import Dict
  import torch
  from coqpit import Coqpit
  from trainer import TrainerModel
label_desc: Use image node_id 2 for calling the BaseTrainerModel obj's underlying member method code with example usage: obj.inference(input, aux_input) and returns: outputs_dict