code: def encode(self, c):
      device = next(self.parameters()).device
      text = self.tokenizer(
          c,
          truncation=True,
          max_length=77,
          return_length=False,
          return_overflowing_tokens=False,
          padding="max_length",
          return_tensors="pt",
      )
      text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
      text["attention_mask"] = torch.tensor(text["attention_mask"]).to(
          device
      )
      features = self(**text)
      return features["projection_state"]
  
file_path: stable-diffusion-webui/modules/xlmr_m18.py
imports: from transformers import BertPreTrainedModel, BertConfig
  import torch.nn
  import torch
  from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
  from transformers import XLMRobertaModel, XLMRobertaTokenizer
  from typing import Optional
label_desc: Use image node_id 2 for calling the BertSeriesModelWithTransformation obj's underlying member method code with example usage: obj.encode(c) and returns: features