class_docstr: Gets HuggingFace based model to be used as an Decoder in NeMo NLP.
  Use the model_name arg to get a named model architecture. 
  Available model names can be found with get_huggingface_pretrained_lm_models_list() or
  by going to https://huggingface.co/models.
  Use the pretrained arg to get the named model architecture with or without pretrained weights.
  
  If model_name is None, then we can pass in a custom configuration via the config_dict.
  For example, to instantiate a HuggingFace BERT model with custom configuration we would do:
      config_dict={
          '_target_': 'transformers.BertConfig',
          'hidden_size': 1536
      } 
  
  
  Args:
      model_name (Optional[str]): Named model architecture from HuggingFace. Defaults to None.
      pretrained (bool): Use True to get pretrained weights. 
                                  False will use the same architecture but with randomly initialized weights.
                                  Defaults to False.
      config_dict (Optional[dict], optional): Use for custom configuration of the HuggingFace model. Defaults to None.
      checkpoint_file (Optional[str], optional): Provide weights for the transformer from a local checkpoint. Defaults to None.
class_docstr_tok: ["Gets","HuggingFace","based","model","to","be","used","as","an","Decoder","in","NeMo","NLP",".","Use","the","model_name","arg","to","get","a","named","model","architecture",".","Available","model","names","can","be","found","with","get_huggingface_pretrained_lm_models_list","(",")","or","by","going","to","https",":","\/\/huggingface.co\/models",".","Use","the","pretrained","arg","to","get","the","named","model","architecture","with","or","without","pretrained","weights",".","If","model_name","is","None",",","then","we","can","pass","in","a","custom","configuration","via","the","config_dict",".","For","example",",","to","instantiate","a","HuggingFace","BERT","model","with","custom","configuration","we","would","do",":","config_dict=","{","'_target_","'",":","'transformers.BertConfig","'",",","'hidden_size","'",":","1536","}","Args",":","model_name","(","Optional","[","str","]",")",":","Named","model","architecture","from","HuggingFace",".","Defaults","to","None",".","pretrained","(","bool",")",":","Use","True","to","get","pretrained","weights",".","False","will","use","the","same","architecture","but","with","randomly","initialized","weights",".","Defaults","to","False",".","config_dict","(","Optional","[","dict","]",",","optional",")",":","Use","for","custom","configuration","of","the","HuggingFace","model",".","Defaults","to","None",".","checkpoint_file","(","Optional","[","str","]",",","optional",")",":","Provide","weights","for","the","transformer","from","a","local","checkpoint",".","Defaults","to","None","."]
code: def hidden_size(self) -> Optional[int]:
      return self._hidden_size
  
file_path: NeMo/nemo/collections/nlp/modules/common/huggingface/huggingface_decoder.py
imports: from typing import Optional
  from hydra.utils import instantiate
  from transformers import AutoConfig, AutoModel
  from nemo.collections.nlp.modules.common.decoder_module import DecoderModule
  from nemo.collections.nlp.modules.common.huggingface.huggingface_utils import get_huggingface_pretrained_lm_models_list
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the HuggingFaceDecoderModule obj's underlying member method code with example usage: obj.hidden_size() and returns: self