class_docstr: BaseTrainerModel model expanding TrainerModel with required functions by 🐸TTS.
  
  Every new 🐸TTS model must inherit it.
docstr: Load a model checkpoint gile and get ready for training or inference.
  
  Args:
      config (Coqpit): Model configuration.
      checkpoint_path (str): Path to the model checkpoint file.
      eval (bool, optional): If true, init model for inference else for training. Defaults to False.
      strict (bool, optional): Match all checkpoint keys to model's keys. Defaults to True.
      cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False.
code: def load_checkpoint(
      self,
      config: Coqpit,
      checkpoint_path: str,
      eval: bool = False,
      strict: bool = True,
      cache=False,
  ) -> None:
      """Load a model checkpoint gile and get ready for training or inference.
  
      Args:
          config (Coqpit): Model configuration.
          checkpoint_path (str): Path to the model checkpoint file.
          eval (bool, optional): If true, init model for inference else for training. Defaults to False.
          strict (bool, optional): Match all checkpoint keys to model's keys. Defaults to True.
          cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False.
      """
      ...
  
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 3 for calling the BaseTrainerModel obj's underlying member method code with example usage: obj.load_checkpoint(config, checkpoint_path, eval, strict, cache) without return types