code: def __init__(
      self,
      vocab_size=50257,
      units=768,
      num_layers=12,
      num_heads=12,
      max_length=1024,
      pos_embed_type="learned",
      activation="gelu(tanh)",
      layer_norm_eps=1e-5,
      embed_dropout_prob=0.1,
      hidden_dropout_prob=0.1,
      attention_dropout_prob=0.1,
      embed_initializer=TruncNorm(stdev=0.02),
      weight_initializer=TruncNorm(stdev=0.02),
      bias_initializer="zeros",
      dtype="float32",
      output_all_encodings=False,
      layout="NT",
      compute_layout="auto",
  ):
      super().__init__()
      self._vocab_size = vocab_size
      self._units = units
      self._num_layers = num_layers
      self._num_heads = num_heads
      self._max_length = max_length
      self._pos_embed_type = pos_embed_type
      self._activation = activation
      self._layer_norm_eps = layer_norm_eps
      self._embed_dropout_prob = embed_dropout_prob
      self._hidden_dropout_prob = hidden_dropout_prob
      self._attention_dropout_prob = attention_dropout_prob
      self._embed_initializer = embed_initializer
      self._weight_initializer = weight_initializer
      self._bias_initializer = bias_initializer
      self._dtype = dtype
      self._layout = layout
      if compute_layout == "auto" or compute_layout is None:
          self._compute_layout = layout
      else:
          self._compute_layout = compute_layout
      self._embed = nn.Embedding(
          input_dim=self._vocab_size,
          output_dim=self._units,
          weight_initializer=embed_initializer,
          dtype=self._dtype,
      )
      self._embed_dropout = nn.Dropout(self._embed_dropout_prob)
      self._pos_embed = PositionalEmbedding(
          units=self._units,
          max_length=self._max_length,
          dtype=self._dtype,
          method=pos_embed_type,
      )
      self._layers = nn.HybridSequential()
      for layer_idx in range(self._num_layers):
          self._layers.add(
              GPT2Layer(
                  units=self._units,
                  num_heads=self._num_heads,
                  layer_norm_eps=self._layer_norm_eps,
                  use_qkv_bias=True,
                  hidden_dropout_prob=self._hidden_dropout_prob,
                  attention_dropout_prob=self._attention_dropout_prob,
                  weight_initializer=self._weight_initializer,
                  bias_initializer=self._bias_initializer,
                  activation=self._activation,
                  dtype=self._dtype,
                  layout=self._compute_layout,
              )
          )
      self._final_ln = nn.LayerNorm(
          epsilon=layer_norm_eps, in_channels=units
      )
  
imports: import os
  from typing import Tuple
  import mxnet
  from mxnet import use_np, np, npx
  from mxnet.gluon import HybridBlock, nn
  from .base import BACKBONE_REGISTRY
  from ..base import get_model_zoo_home_dir, get_repo_model_zoo_url, get_model_zoo_checksum_dir
  from ..utils.config import CfgNode
  from ..utils.misc import load_checksum_stats, download
  from ..utils.registry import Registry
  from ..initializer import TruncNorm
  from ..attention_cell import MultiHeadAttentionCell
  from ..layers import get_activation, PositionalEmbedding
  from ..data.tokenizers import HuggingFaceByteBPETokenizer
label_desc: Use image node_id 1 to create a new GPT2Model object from inherited base classes: HybridBlock with example: obj = GPT2Model(vocab_size, units, num_layers, num_heads, max_length, pos_embed_type, activation, layer_norm_eps, embed_dropout_prob, hidden_dropout_prob, attention_dropout_prob, embed_initializer, weight_initializer, bias_initializer, dtype, output_all_encodings, layout, compute_layout)