code: def __init__(
      self,
      embed_dim,
      nhead=8,
      dim_mlp=2048,
      dropout=0.0,
      activation="gelu",
  ):
      super().__init__()
      self.self_attn = nn.MultiheadAttention(
          embed_dim, nhead, dropout=dropout
      )
      # Implementation of Feedforward model - MLP
      self.linear1 = nn.Linear(embed_dim, dim_mlp)
      self.dropout = nn.Dropout(dropout)
      self.linear2 = nn.Linear(dim_mlp, embed_dim)
  
      self.norm1 = nn.LayerNorm(embed_dim)
      self.norm2 = nn.LayerNorm(embed_dim)
      self.dropout1 = nn.Dropout(dropout)
      self.dropout2 = nn.Dropout(dropout)
  
      self.activation = _get_activation_fn(activation)
  
imports: import math
  import torch
  from torch import nn, Tensor
  import torch.nn.functional
  from typing import Optional
  from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
  from basicsr.utils.registry import ARCH_REGISTRY
label_desc: Use image node_id 1 to create a new TransformerSALayer object from inherited base classes: nn with example: obj = TransformerSALayer(embed_dim, nhead, dim_mlp, dropout, activation)