code: def __init__(self, config=None, **kargs):
      # modify initialization for autoloading
      if config is None:
          config = XLMRobertaConfig()
          config.attention_probs_dropout_prob = 0.1
          config.bos_token_id = 0
          config.eos_token_id = 2
          config.hidden_act = "gelu"
          config.hidden_dropout_prob = 0.1
          config.hidden_size = 1024
          config.initializer_range = 0.02
          config.intermediate_size = 4096
          config.layer_norm_eps = 1e-05
          config.max_position_embeddings = 514
  
          config.num_attention_heads = 16
          config.num_hidden_layers = 24
          config.output_past = True
          config.pad_token_id = 1
          config.position_embedding_type = "absolute"
  
          config.type_vocab_size = 1
          config.use_cache = True
          config.vocab_size = 250002
          config.project_dim = 768
          config.learn_encoder = False
      super().__init__(config)
      self.roberta = XLMRobertaModel(config)
      self.transformation = nn.Linear(
          config.hidden_size, config.project_dim
      )
      self.pre_LN = nn.LayerNorm(
          config.hidden_size, eps=config.layer_norm_eps
      )
      self.tokenizer = XLMRobertaTokenizer.from_pretrained(
          "xlm-roberta-large"
      )
      self.pooler = lambda x: x[:, 0]
      self.post_init()
  
file_path: stable-diffusion-webui/modules/xlmr.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 1 to create a new BertSeriesModelWithTransformation object from inherited base classes: BertPreTrainedModel with example: obj = BertSeriesModelWithTransformation(config)