docstr: :param input_features:  [NxCxHxW]  featuers comming from the shape branch (canny branch).
  :param gating_features: [Nx1xHxW] features comming from the texture branch (resnet). Only one channel feature map.
  :return:
docstr_tok: [":","param","input_features",":","[","NxCxHxW","]","featuers","comming","from","the","shape","branch","(","canny","branch",")",".",":","param","gating_features",":","[","Nx1xHxW","]","features","comming","from","the","texture","branch","(","resnet",")",".","Only","one","channel","feature","map",".",":","return",":"]
code: def forward(self, input_features, gating_features):
      """
  
      :param input_features:  [NxCxHxW]  featuers comming from the shape branch (canny branch).
      :param gating_features: [Nx1xHxW] features comming from the texture branch (resnet). Only one channel feature map.
      :return:
      """
      n, c, h, w = input_features.size()
      smooth_features = self.gaussian_filter(input_features)
      dog_features = input_features - smooth_features
      dog_features = self.cw(
          torch.cat((dog_features, input_features), dim=1)
      )
  
      alphas = self._gate_conv(
          torch.cat([input_features, gating_features], dim=1)
      )
  
      dog_features = dog_features * (alphas + 1)
  
      return F.conv2d(
          dog_features,
          self.weight,
          self.bias,
          self.stride,
          self.padding,
          self.dilation,
          self.groups,
      )
  
imports: import torch.nn
  import torch
  import torch.nn.functional
  from torch.nn.modules.conv import _ConvNd
  from torch.nn.modules.utils import _pair
  import numpy
  import math
  from ..mynn import Norm2d
  from .custom_functional import conv2d_same
label_desc: Use image node_id 2 for calling the HighFrequencyGatedSpatialConv2d obj's underlying member method code with example usage: obj.forward(input_features, gating_features) and returns: F