def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
        """
        Activates gradient checkpointing for the current model.

        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".

        We pass the `__call__` method of the modules instead of `forward` because `__call__` attaches all the hooks of
        the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2

        Args:
            gradient_checkpointing_kwargs (dict, *optional*):
                Additional keyword arguments passed along to the `torch.utils.checkpoint.checkpoint` function.
        """
        if not self.supports_gradient_checkpointing:
            raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")

        if gradient_checkpointing_kwargs is None:
            gradient_checkpointing_kwargs = {}

        gradient_checkpointing_func = functools.partial(checkpoint, **gradient_checkpointing_kwargs)

        self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)

        if getattr(self, "_hf_peft_config_loaded", False):
            # When using PEFT + gradient checkpointing + Trainer we need to make sure the input has requires_grad=True
            # we do it also on PEFT: https://github.com/huggingface/peft/blob/85013987aa82aa1af3da1236b6902556ce3e483e/src/peft/peft_model.py#L334
            # When training with PEFT, only LoRA layers will have requires grad set to True, but the output of frozen layers need to propagate
            # the gradients to make sure the gradient flows.
            self.enable_input_require_grads()

        Activates gradient checkpointing for the current model.

        Note that in other frameworks this feature can be referred to as "activation checkpointing" or "checkpoint
        activations".

        We pass the `__call__` method of the modules instead of `forward` because `__call__` attaches all the hooks of
        the module. https://discuss.pytorch.org/t/any-different-between-model-input-and-model-forward-input/3690/2

        Args:
            gradient_checkpointing_kwargs (dict, *optional*):
                Additional keyword arguments passed along to the `torch.utils.checkpoint.checkpoint` function.
        
dpkd/transformers/src/transformers/modeling_utils.py