Support for Reformer

Even though ReformerForSequenceClassification follows the "{MODEL_NAME}ForSequenceClassification" pattern as indicated in the documentation, I end up with a RuntimeError as follows. Since it's so simple to use the Explainers, I'm assuming Reformer models aren't supported yet.

Here's the last frame in the trace in the Explainer code:
File ~/.local/lib/python3.10/site-packages/transformers_interpret/explainer.py:197, in BaseExplainer._get_preds(self, input_ids, token_type_ids, position_ids, attention_mask)
194 return preds
196 elif self.accepts_position_ids:
--> 197 preds = self.model(
198 input_ids=input_ids,
199 position_ids=position_ids,
200 attention_mask=attention_mask,
201 )
203 return preds
204 elif self.accepts_token_type_ids:

Here's the last frame where error occured:
File ~/.local/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2174, in ReformerModel._pad_to_mult_of_chunk_length(self, input_ids, inputs_embeds, attention_mask, position_ids, input_shape, padding_length, padded_seq_length, device)
2172 # Pad position ids if given
2173 if position_ids is not None:
-> 2174 padded_position_ids = torch.arange(input_shape[-1], padded_seq_length, dtype=torch.long, device=device)
2175 padded_position_ids = position_ids.unsqueeze(0).expand(input_shape[0], padding_length)
2176 position_ids = torch.cat([position_ids, padded_position_ids], dim=-1)

RuntimeError: upper bound and larger bound inconsistent with step sign

I received the exact same error and stack trace with both SequenceClassificationExplainer and MultiLabelClassificationExplainer which I tried because I am trying to do multi-label text classification.

Please let me know if you need the full trace or any other information. Thank you.