code: def __init__(self, config):
      super().__init__(config)
  
      self.transformer = T5Model(config)
      init(self.transformer)
  
      self._init_head_modules()
  
      self.init_weights()
  
      # Model parallel
      self.model_parallel = False
      self.device_map = None
  
imports: import logging
  import torch
  from transformers.models.t5.modeling_t5 import T5_INPUTS_DOCSTRING, T5_START_DOCSTRING, T5Model, T5PreTrainedModel
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward
  from ...composition import adjust_tensors_for_parallel
  from ...heads import ClassificationHead, ModelWithFlexibleHeadsAdaptersMixin, MultiLabelClassificationHead, QuestionAnsweringHead, Seq2SeqLMHead
  from ...model_mixin import EmbeddingAdaptersWrapperMixin
  from ...wrappers import init
label_desc: Use image node_id 1 to create a new T5AdapterModel object from inherited base classes: EmbeddingAdaptersWrapperMixin, ModelWithFlexibleHeadsAdaptersMixin, T5PreTrainedModel with example: obj = T5AdapterModel(config)