code: def __init__(self, config: BioGptConfig):
      super().__init__(config)
      self.config = config
      self.layerdrop = config.layerdrop
      self.dropout = config.hidden_dropout_prob
      self.embed_dim = config.hidden_size
      self.padding_idx = config.pad_token_id
      self.embed_scale = (
          math.sqrt(config.hidden_size)
          if config.scale_embedding
          else 1.0
      )
  
      self.embed_tokens = nn.Embedding(
          config.vocab_size, self.embed_dim, self.padding_idx
      )
      self.embed_positions = BioGptLearnedPositionalEmbedding(
          config.max_position_embeddings, self.embed_dim
      )
  
      self.layers = nn.ModuleList(
          [
              BioGptDecoderLayer(config)
              for _ in range(config.num_hidden_layers)
          ]
      )
      self.layer_norm = nn.LayerNorm(self.embed_dim)
  
      self.gradient_checkpointing = False
      # Initialize weights and apply final processing
      self.post_init()
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, SequenceClassifierOutputWithPast, TokenClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_biogpt import BioGptConfig
label_desc: Use image node_id 1 to create a new BioGptModel object from inherited base classes: BioGptPreTrainedModel with example: obj = BioGptModel(config)