code: def forward(
      self,
      hidden_states: torch.Tensor,
      attention_mask: torch.Tensor,
      encoder_output: Optional[torch.Tensor] = None,
      enc_dec_attn_mask: Optional[torch.Tensor] = None,
      inference_params: Optional[Any] = None,
      is_first_microbatch: Optional[bool] = None,
      checkpoint_core_attention: Optional[bool] = False,
  ) -> torch.Tensor:
      if self.dtype == torch.float32:
          return super().forward(
              hidden_states,
              attention_mask,
              encoder_output=encoder_output,
              enc_dec_attn_mask=enc_dec_attn_mask,
              inference_params=inference_params,
              is_first_microbatch=is_first_microbatch,
              checkpoint_core_attention=checkpoint_core_attention,
          )
      with torch.autocast(device_type="cuda", dtype=self.dtype):
          return super().forward(
              hidden_states,
              attention_mask,
              encoder_output=encoder_output,
              enc_dec_attn_mask=enc_dec_attn_mask,
              inference_params=inference_params,
              is_first_microbatch=is_first_microbatch,
              checkpoint_core_attention=checkpoint_core_attention,
          )
  
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 2 for calling the AutocastTransformerLayer obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, encoder_output, enc_dec_attn_mask, inference_params, is_first_microbatch, checkpoint_core_attention) and returns: super, super