class LDMBertPreTrainedModel(PreTrainedModel):
    config_class = LDMBertConfig
    base_model_prefix = "model"
    _supports_gradient_checkpointing = True
    _keys_to_ignore_on_load_unexpected = [r"encoder\.version", r"decoder\.version"]

    def _init_weights(self, module):
        std = self.config.init_std
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (LDMBertEncoder,)):
            module.gradient_checkpointing = value

    @property
    def dummy_inputs(self):
        pad_token = self.config.pad_token_id
        input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
        dummy_inputs = {
            "attention_mask": input_ids.ne(pad_token),
            "input_ids": input_ids,
        }
        return dummy_inputs
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py