Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length
To train a Bert LM, I updated the DataCollatorForLanguageModeling function, and it creates another input_ids  as  new_input_ids which is the same size as input_ids. To train bert model it accepts input_ids and new_input_ids in BertForPreTraining class. But facing issues in batch size, getting erroras ’ Unable to create tensor, you should probably activate truncation and/or padding with ‘padding=True’ ‘truncation=True’ to have batched tensors with the same length’I get this issue, comes from this 699 line of this filegithub.comhuggingface/transformers/blob/bd9871657bb9500a9f4437a873db6df5f1ae6dbb/src/transformers/tokenization_utils_base.py#L715# elif tensor.ndim < 2:#     tensor = tensor[None, :]self[key] = tensorexcept:  # noqa E722if key == "overflowing_tokens":raise ValueError("Unable to create tensor returning overflowing tokens of different lengths. ""Please see if a fast version of this tokenizer is available to have this feature available.")raise ValueError("Unable to create tensor, you should probably activate truncation and/or padding ""with 'padding=True' 'truncation=True' to have batched tensors with the same length.")return self@torch_requireddef to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":"""Send all values to device by calling :obj:`v.to(device)` (PyTorch only).Now how can i keep the same size of new_input_ids same as input_ids for every batch