class_docstr: Transformer backend wih Huggingface transformer models
class_docstr_tok: ["Transformer","backend","wih","Huggingface","transformer","models"]
docstr: Build the transformer base model.
docstr_tok: ["Build","the","transformer","base","model","."]
code: def build_transformer_base(self):
      """Build the transformer base model."""
      hf_params = {"config": self.transformer_config}
  
      # For BERT models, initialize using Jit version
      if self.config.transformer_base.startswith("bert-"):
          self.transformer = BertModelJit.from_pretrained(
              self.config.transformer_base, **hf_params
          )
      else:
          self.transformer = AutoModel.from_pretrained(
              self.config.transformer_base, **hf_params
          )
  
imports: from copy import deepcopy
  from typing import Any, Dict, List, Type
  import torch
  from mmf.common.registry import registry
  from mmf.models.transformers.base import BaseTransformer, BaseTransformerBackend
  from mmf.modules.hf_layers import BertModelJit, replace_with_jit
  from omegaconf import OmegaConf
  from torch import nn, Tensor
label_desc: Use image node_id 3 for calling the HuggingfaceBackend obj's underlying member method code with example usage: obj.build_transformer_base() without return types