code: def __init__(self, config):
      super().__init__(config)
      setattr(self, "embeddings", Embeddings(config))
      setattr(self, "transformer", Transformer(config))
  
      # Register hook for unsqueezing nn.Linear parameters to match nn.Conv2d parameter spec
      self._register_load_state_dict_pre_hook(linear_to_conv2d_map)
  
imports: from ane_transformers.reference.layer_norm import LayerNormANE
  import torch
  import torch.nn
  from transformers.models.distilbert import modeling_distilbert
label_desc: Use image node_id 1 to create a new DistilBertModel object from inherited base classes: modeling_distilbert with example: obj = DistilBertModel(config)