content: import torch
  import torch.nn as nn
  from mmcv.cnn import normal_init
  
  from ..registry import HEADS
  from .base import BaseHead
  
  
  @HEADS.register_module()
  class SlowFastRPLHead(BaseHead):
      """The classification head for SlowFast.
  
      Args:
          num_classes (int): Number of classes to be classified.
          in_channels (int): Number of channels in input feature.
          loss_cls (dict): Config for building loss.
              Default: dict(type='CrossEntropyLoss').
          spatial_type (str): Pooling type in spatial dimension. Default: 'avg'.
          dropout_ratio (float): Probability of dropout layer. Default: 0.8.
          init_std (float): Std value for Initiation. Default: 0.01.
          kwargs (dict, optional): Any keyword argument to be used to initialize
              the head.
      """
  
      def __init__(self,
                   num_classes,
                   in_channels,
                   loss_cls=dict(type='RPLoss'),
                   spatial_type='avg',
                   dropout_ratio=0.8,
                   init_std=0.01,
                   num_centers=1,
                   **kwargs):
  
          super().__init__(num_classes, in_channels, loss_cls, **kwargs)
          self.spatial_type = spatial_type
          self.dropout_ratio = dropout_ratio
          self.init_std = init_std
          self.num_centers = num_centers
  
          if self.dropout_ratio != 0:
              self.dropout = nn.Dropout(p=self.dropout_ratio)
          else:
              self.dropout = None
          self.fc_centers = nn.Linear(self.in_channels, self.num_classes * self.num_centers, bias=False)
  
          if self.spatial_type == 'avg':
              self.avg_pool = nn.AdaptiveAvgPool3d((1, 1, 1))
          else:
              self.avg_pool = None
  
      def init_weights(self):
          """Initiate the parameters from scratch."""
          normal_init(self.fc_centers, std=self.init_std)
  
      def compute_dist(self, features, center=None, metric='fc'):
          if metric == 'l2':
              f_2 = torch.sum(torch.pow(features, 2), dim=1, keepdim=True)
              if center is None:
                  c_2 = torch.sum(torch.pow(self.fc_centers.weight, 2), dim=1, keepdim=True)
                  dist = f_2 - 2 * self.fc_centers(features) + torch.transpose(c_2, 1, 0)
              else:
                  c_2 = torch.sum(torch.pow(center, 2), dim=1, keepdim=True)
                  dist = f_2 - 2*torch.matmul(features, torch.transpose(center, 1, 0)) + torch.transpose(c_2, 1, 0)
              dist = dist / float(features.shape[1])
          else:
              if center is None:
                  dist = self.fc_centers(features)
              else:
                  dist = features.matmul(center.t())
          dist = torch.reshape(dist, [-1, self.num_classes, self.num_centers])
          dist = torch.mean(dist, dim=2)
          return dist
  
      def forward(self, x):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
  
          Returns:
              torch.Tensor: The classification scores for input samples.
          """
          # ([N, channel_fast, T, H, W], [(N, channel_slow, T, H, W)])
          x_fast, x_slow = x
          # ([N, channel_fast, 1, 1, 1], [N, channel_slow, 1, 1, 1])
          x_fast = self.avg_pool(x_fast)
          x_slow = self.avg_pool(x_slow)
          # [N, channel_fast + channel_slow, 1, 1, 1]
          x = torch.cat((x_slow, x_fast), dim=1)
  
          if self.dropout is not None:
              x = self.dropout(x)
  
          # [N x C]
          x = x.view(x.size(0), -1)
          # [N x num_classes]
          dist = self.compute_dist(x)
          # [N, num_classes]
          if self.loss_cls.__class__.__name__ == 'GCPLoss':
              dist = -dist
          outputs = {'dist': dist, 'feature': x, 'centers': self.fc_centers.weight}
  
          return outputs
  