code: def __init__(
      self,
      dim: int,
      focal_window,
      focal_level: int,
      focal_factor: int = 2,
      bias: bool = True,
      use_post_norm: bool = False,
      normalize_modulator: bool = False,
      proj_drop: float = 0.0,
      norm_layer: Callable = LayerNorm2d,
  ):
      super().__init__()
  
      self.dim = dim
      self.focal_window = focal_window
      self.focal_level = focal_level
      self.focal_factor = focal_factor
      self.use_post_norm = use_post_norm
      self.normalize_modulator = normalize_modulator
      self.input_split = [dim, dim, self.focal_level + 1]
  
      self.f = nn.Conv2d(
          dim,
          2 * dim + (self.focal_level + 1),
          kernel_size=1,
          bias=bias,
      )
      self.h = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
  
      self.act = nn.GELU()
      self.proj = nn.Conv2d(dim, dim, kernel_size=1)
      self.proj_drop = nn.Dropout(proj_drop)
      self.focal_layers = nn.ModuleList()
  
      self.kernel_sizes = []
      for k in range(self.focal_level):
          kernel_size = self.focal_factor * k + self.focal_window
          self.focal_layers.append(
              nn.Sequential(
                  nn.Conv2d(
                      dim,
                      dim,
                      kernel_size=kernel_size,
                      groups=dim,
                      padding=kernel_size // 2,
                      bias=False,
                  ),
                  nn.GELU(),
              )
          )
          self.kernel_sizes.append(kernel_size)
      self.norm = (
          norm_layer(dim) if self.use_post_norm else nn.Identity()
      )
  
imports: from functools import partial
  from typing import Callable, Optional, Tuple
  import torch
  import torch.nn
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import Mlp, DropPath, LayerNorm2d, trunc_normal_, ClassifierHead, NormMlpClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import generate_default_cfgs, register_model
label_desc: Use image node_id 1 to create a new FocalModulation object from inherited base classes: nn with example: obj = FocalModulation(dim, focal_window, focal_level, focal_factor, bias, use_post_norm, normalize_modulator, proj_drop, norm_layer)