code: def __init__(
      self,
      in_channels: list[int],
      hidden_dim: int,
      dim_feedforward: int,
      expansion: float = 1.0,
  ) -> None:
      super().__init__()
      self.input_proj = nn.ModuleList(
          [
              ConvNormAct(in_ch, hidden_dim, 1, act="none")
              for in_ch in in_channels
          ]
      )
      self.aifi = AIFI(hidden_dim, 8, dim_feedforward)
      self.ccfm = CCFM(len(in_channels), hidden_dim, expansion)
  
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 HybridEncoder object from inherited base classes: Module with example: obj = HybridEncoder(in_channels, hidden_dim, dim_feedforward, expansion)