code: def __init__(
      self,
      hidden_size: int,
      ffn_hidden_size: int,
      layernorm_epsilon: float,
      num_attention_heads: int,
      init_method: Callable,
      output_layer_init_method: Callable,
      hidden_dropout: float,
      attention_dropout: float,
      layer_number: Optional[int] = None,
      kv_channels: Optional[int] = None,
      self_attn_mask_type: str = "causal",
      tp_group: Optional[Any] = None,
      tp_size: int = 1,
      params_dtype: torch.dtype = torch.float32,
      get_rng_state_tracker: Optional[Callable] = None,
      fuse_wgrad_accumulation: bool = False,
      seq_length: Optional[int] = None,
      micro_batch_size: Optional[int] = None,
      sequence_parallel: bool = False,
      apply_residual_connection_post_layernorm: bool = False,
      output_layernorm: bool = False,
      layer_type: str = "encoder",
      drop_path_rate: float = 0,
      use_emha: bool = False,
      ub_tp_comm_overlap: bool = False,
      autocast_dtype: Any = 16,
      zero_centered_gamma: bool = False,
      device: str = "cuda",
  ) -> None:
      super().__init__(
          hidden_size=hidden_size,
          ffn_hidden_size=ffn_hidden_size,
          layernorm_epsilon=layernorm_epsilon,
          num_attention_heads=num_attention_heads,
          init_method=init_method,
          output_layer_init_method=output_layer_init_method,
          hidden_dropout=hidden_dropout,
          attention_dropout=attention_dropout,
          layer_number=layer_number,
          kv_channels=kv_channels,
          self_attn_mask_type=self_attn_mask_type,
          tp_group=tp_group,
          tp_size=tp_size,
          params_dtype=params_dtype,
          get_rng_state_tracker=get_rng_state_tracker,
          fuse_wgrad_accumulation=fuse_wgrad_accumulation,
          seq_length=seq_length,
          micro_batch_size=micro_batch_size,
          sequence_parallel=sequence_parallel,
          apply_residual_connection_post_layernorm=apply_residual_connection_post_layernorm,
          output_layernorm=output_layernorm,
          layer_type=layer_type,
          drop_path_rate=drop_path_rate,
          set_parallel_mode=tp_size > 1,
          fuse_qkv_params=True,
          zero_centered_gamma=zero_centered_gamma,
          ub_tp_comm_overlap=ub_tp_comm_overlap,
          device=device,
      )
      # use_emha=use_emha,
  
      # Dtype for forward pass - ignore amp O2
      self.dtype = utils_funcs.torch_dtype_from_precision(
          autocast_dtype, megatron_amp_O2=None
      )
  
imports: from contextlib import nullcontext
  from typing import Any, Callable, Optional
  import torch
  import torch.nn
  from einops import rearrange
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.collections.nlp.modules.common.megatron.adapters.parallel_adapters import AdapterName, ParallelLinearAdapterConfig, ParallelLinearAdapterWeightTyingConfig
  from nemo.collections.nlp.modules.common.megatron.attention import ParallelAttention, ParallelChunkedCrossAttention
  from nemo.collections.nlp.modules.common.megatron.fused_bias_dropout_add import bias_dropout_add, bias_dropout_add_fused_inference, bias_dropout_add_fused_train, dropout_add
  from nemo.collections.nlp.modules.common.megatron.fused_layer_norm import get_layer_norm
  from nemo.collections.nlp.modules.common.megatron.layer_norm_1p import LayerNorm1P, LPLayerNorm
  from nemo.collections.nlp.modules.common.megatron.layer_type import LayerType
  from nemo.collections.nlp.modules.common.megatron.mlp import ParallelMLP, SwitchMLP
  from nemo.collections.nlp.modules.common.megatron.module import MegatronModule
  from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
  from nemo.collections.nlp.parts import utils_funcs
  from nemo.core import adapter_mixins
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new AutocastTransformerLayer object from inherited base classes: TransformerLayer with example: obj = AutocastTransformerLayer(hidden_size, ffn_hidden_size, layernorm_epsilon, num_attention_heads, init_method, output_layer_init_method, hidden_dropout, attention_dropout, layer_number, kv_channels, _attn_mask_type, tp_group, tp_size, params_dtype, get_rng_state_tracker, fuse_wgrad_accumulation, seq_length, micro_batch_size, sequence_parallel, apply_residual_connection_post_layernorm, output_layernorm, layer_type, drop_path_rate, use_emha, ub_tp_comm_overlap, autocast_dtype, zero_centered_gamma, device)