code: def t():
      import matplotlib.pyplot as plt
  
      canny_map_filters_in = 8
      canny_map = np.random.normal(
          size=(1, canny_map_filters_in, 10, 10)
      )  # NxCxHxW
      resnet_map = np.random.normal(size=(1, 1, 10, 10))  # NxCxHxW
      plt.imshow(canny_map[0, 0])
      plt.show()
  
      canny_map = torch.from_numpy(canny_map).float()
      resnet_map = torch.from_numpy(resnet_map).float()
  
      gconv = GatedSpatialConv2d(
          canny_map_filters_in,
          canny_map_filters_in,
          kernel_size=3,
          stride=1,
          padding=1,
      )
      output_map = gconv(canny_map, resnet_map)
      print("done")
  
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 1 for calling a global function with example usage: t() without return types