code: def __init__(self, config):
      super().__init__(config)
  
      self.roberta = XmodModel(config)
      init(self.roberta)
  
      self._init_head_modules()
  
      self.init_weights()
  
imports: from typing import Optional
  import torch
  from transformers.models.xmod.modeling_xmod import XMOD_INPUTS_DOCSTRING, XMOD_START_DOCSTRING, XmodModel, XmodPreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...context import AdapterSetup
  from ...heads import BertStyleMaskedLMHead, BiaffineParsingHead, CausalLMHead, ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, MultipleChoiceHead, QuestionAnsweringHead, TaggingHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 1 to create a new XmodAdapterModel object from inherited base classes: EmbeddingAdaptersWrapperMixin, ModelWithFlexibleHeadsAdaptersMixin, XmodPreTrainedModel with example: obj = XmodAdapterModel(config)