code: def __init__(
      self,
      embed_dim: int,
      num_heads: int,
      dim_feedforward: int,
      dropout: float = 0.0,
  ) -> None:
      super().__init__()
      self.self_attn = nn.MultiheadAttention(
          embed_dim, num_heads, dropout
      )  # NOTE: batch_first = False
      self.dropout1 = nn.Dropout(dropout)
      self.norm1 = nn.LayerNorm(embed_dim)
  
      self.linear1 = nn.Linear(embed_dim, dim_feedforward)
      self.act = nn.GELU()
      self.dropout = nn.Dropout(dropout)
      self.linear2 = nn.Linear(dim_feedforward, embed_dim)
      self.dropout2 = nn.Dropout(dropout)
      self.norm2 = nn.LayerNorm(embed_dim)
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.fusion import fuse_conv_bn_weights
  from kornia.contrib.models.common import ConvNormAct
  from kornia.core import Module, Tensor, concatenate, pad
label_desc: Use image node_id 1 to create a new AIFI object from inherited base classes: Module with example: obj = AIFI(embed_dim, num_heads, dim_feedforward, dropout)