code: def forward(self, x):
      # input is RGB image with shape [B 3 H W]
      x = F.pad(x, pad=(1, 1, 1, 1), mode="reflect")
      gx_rgb = F.conv2d(
          x, self.weight_x, bias=None, stride=1, padding=0, groups=3
      )
      gy_rgb = F.conv2d(
          x, self.weight_y, bias=None, stride=1, padding=0, groups=3
      )
      norm_rgb = torch.stack([gx_rgb, gy_rgb], dim=-1).norm(dim=-1)
      phase = torch.atan2(gx_rgb, gy_rgb)
      phase = phase / self.pi * self.nbins  # [-9, 9]
  
      b, c, h, w = norm_rgb.shape
      out = torch.zeros(
          (b, c, self.nbins, h, w), dtype=torch.float, device=x.device
      )
      phase = phase.view(b, c, 1, h, w)
      norm_rgb = norm_rgb.view(b, c, 1, h, w)
      if self.gaussian_window:
          if h != self.gaussian_window:
              assert h % self.gaussian_window == 0, "h {} gw {}".format(
                  h, self.gaussian_window
              )
              repeat_rate = h // self.gaussian_window
              temp_gkern = self.gkern.repeat([repeat_rate, repeat_rate])
          else:
              temp_gkern = self.gkern
          norm_rgb *= temp_gkern
  
      out.scatter_add_(2, phase.floor().long() % self.nbins, norm_rgb)
  
      out = out.unfold(3, self.pool, self.pool)
      out = out.unfold(4, self.pool, self.pool)
      out = out.sum(dim=[-1, -2])
  
      out = torch.nn.functional.normalize(out, p=2, dim=2)
  
      return out
  
file_path: SlowFast/slowfast/models/operators.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision
  from slowfast.models.utils import get_gkern
  from pytorchvideo.layers.swish import Swish
label_desc: Use image node_id 2 for calling the HOGLayerC obj's underlying member method code with example usage: obj.forward(x) and returns: out