def init_training_args(self) -> None:
        """
        This method is used to generate the `spacy` configuration file, which is used to train
        """
        from spacy.cli.init_config import init_config

        # We generate the config with GPU just when we are using `spacy-transformers`,
        # otherwise the default configuration will be messed up for `spacy`.
        self.trainer_kwargs = init_config(
            lang=self.language,
            pipeline=self._spacy_pipeline_components,
            optimize=self.optimize,
            gpu=False,
        )

        self.trainer_kwargs["paths"]["train"] = self._train_dataset_path
        self.trainer_kwargs["paths"]["dev"] = self._eval_dataset_path
        self.trainer_kwargs["system"]["seed"] = self._seed or 42

        # Now we can already set the GPU properties if we want to train/fine-tune a
        # `spacy` model using the GPU, or a `spacy-transformers` model using the CPU.
        self.trainer_kwargs["system"]["gpu_allocator"] = (
            ("pytorch" if self.has_torch else "tensorflow" if self.has_tensorflow else None) if self.use_gpu else None
        )
        self.trainer_kwargs["nlp"]["batch_size"] = 128 if self.use_gpu else 1000

        if "tok2vec" in self.trainer_kwargs["nlp"]["pipeline"]:
            # If we want to fine-tune the `tok2vec` component, then we need to set the
            # `init_tok2vec` path to the model we want to fine-tune.
            if self.freeze_tok2vec is False:
                self.trainer_kwargs["paths"]["init_tok2vec"] = self._model
            else:
                # Otherwise, if we don't want to fine-tune the `tok2vec` component, then we
                # need to set the `frozen_components` and `annotating_components` to
                # `["tok2vec"]`.
                self.trainer_kwargs["training"]["frozen_components"] = ["tok2vec"]
                self.trainer_kwargs["training"]["annotating_components"] = ["tok2vec"]

        This method is used to generate the `spacy` configuration file, which is used to train
        
argilla-v1/src/argilla_v1/training/spacy.py