def set_testing(
        self,
        batch_size: int = 8,
        loss_only: bool = False,
        jit_mode: bool = False,
    ):
        """
        A method that regroups all basic arguments linked to testing on a held-out dataset.

        <Tip>

        Calling this method will automatically set `self.do_predict` to `True`.

        </Tip>

        Args:
            batch_size (`int` *optional*, defaults to 8):
                The batch size per device (GPU/TPU core/CPU...) used for testing.
            loss_only (`bool`, *optional*, defaults to `False`):
                Ignores all outputs except the loss.
            jit_mode (`bool`, *optional*):
                Whether or not to use PyTorch jit trace for inference.

        Example:

        ```py
        >>> from transformers import TrainingArguments

        >>> args = TrainingArguments("working_dir")
        >>> args = args.set_testing(batch_size=32)
        >>> args.per_device_eval_batch_size
        32
        ```
        """
        self.do_predict = True
        self.per_device_eval_batch_size = batch_size
        self.prediction_loss_only = loss_only
        self.jit_mode_eval = jit_mode
        return self

        A method that regroups all basic arguments linked to testing on a held-out dataset.

        <Tip>

        Calling this method will automatically set `self.do_predict` to `True`.

        </Tip>

        Args:
            batch_size (`int` *optional*, defaults to 8):
                The batch size per device (GPU/TPU core/CPU...) used for testing.
            loss_only (`bool`, *optional*, defaults to `False`):
                Ignores all outputs except the loss.
            jit_mode (`bool`, *optional*):
                Whether or not to use PyTorch jit trace for inference.

        Example:

        ```py
        >>> from transformers import TrainingArguments

        >>> args = TrainingArguments("working_dir")
        >>> args = args.set_testing(batch_size=32)
        >>> args.per_device_eval_batch_size
        32
        ```
        
dpkd/transformers/src/transformers/training_args.py