code: def __init__(
      self,
      units: int = 768,
      num_heads: int = 12,
      layer_norm_eps: float = 1e-5,
      use_qkv_bias: bool = True,
      hidden_dropout_prob: float = 0.1,
      attention_dropout_prob: float = 0.1,
      weight_initializer=None,
      bias_initializer="zeros",
      dtype="float32",
      layout="NT",
  ):
      super().__init__()
      self._units = units
      self._num_heads = num_heads
      self._layer_norm_eps = layer_norm_eps
      self._use_qkv_bias = use_qkv_bias
      self._hidden_dropout_prob = hidden_dropout_prob
      self._attention_dropout_prob = attention_dropout_prob
      self._weight_initializer = weight_initializer
      self._bias_initializer = bias_initializer
      self._dtype = dtype
      self._layout = layout
      assert layout in ["TN", "NT"], (
          "Invalid layout received = {}. "
          'Only "TN" and "NT" are accepted!'.format(layout)
      )
      self._attention_layout = "NTK" if self._layout == "NT" else "TNK"
  
      self.ln = nn.LayerNorm(
          epsilon=self._layer_norm_eps, in_channels=self._units
      )
      self.qkv = nn.Dense(
          3 * units,
          in_units=units,
          use_bias=use_qkv_bias,
          flatten=False,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          dtype=dtype,
      )
      self.out_proj = nn.Dense(
          units=units,
          in_units=units,
          use_bias=True,
          flatten=False,
          weight_initializer=weight_initializer,
          bias_initializer=bias_initializer,
          dtype=self._dtype,
      )
      self.attention_cell = MultiHeadAttentionCell(
          query_units=self._units,
          num_heads=self._num_heads,
          attention_dropout=self._attention_dropout_prob,
          scaled=True,
          dtype=self._dtype,
          layout=self._attention_layout,
      )
      self.hidden_dropout = nn.Dropout(self._hidden_dropout_prob)
  
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 GPT2SelfAttentionLayer object from inherited base classes: HybridBlock with example: obj = GPT2SelfAttentionLayer(units, num_heads, layer_norm_eps, use_qkv_bias, hidden_dropout_prob, attention_dropout_prob, weight_initializer, bias_initializer, dtype, layout)