text: import torch
  import torch.nn as nn
  from mmcv.cnn import normal_init
  
  from ..registry import HEADS
  from .tsn_head import TSNHead
  
  
  @HEADS.register_module()
  class TPNRPLHead(TSNHead):
      """Class head for TPN.
  
      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'.
          consensus (dict): Consensus config dict.
          dropout_ratio (float): Probability of dropout layer. Default: 0.4.
          init_std (float): Std value for Initiation. Default: 0.01.
          multi_class (bool): Determines whether it is a multi-class
              recognition task. Default: False.
          label_smooth_eps (float): Epsilon used in label smooth.
              Reference: https://arxiv.org/abs/1906.02629. Default: 0.
      """
  
      def __init__(self, num_centers=1, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          self.num_centers = num_centers
          self.fc_centers = nn.Linear(self.in_channels, self.num_classes * self.num_centers, bias=False)
  
          if self.spatial_type == 'avg':
              # use `nn.AdaptiveAvgPool3d` to adaptively match the in_channels.
              self.avg_pool3d = nn.AdaptiveAvgPool3d((1, 1, 1))
          else:
              self.avg_pool3d = None
  
          self.avg_pool2d = 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, num_segs=None):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
              num_segs (int | None): Number of segments into which a video
                  is divided. Default: None.
          Returns:
              torch.Tensor: The classification scores for input samples.
          """
          if self.avg_pool2d is None:
              kernel_size = (1, x.shape[-2], x.shape[-1])
              self.avg_pool2d = nn.AvgPool3d(kernel_size, stride=1, padding=0)
  
          if num_segs is None:
              # [N, in_channels, 3, 7, 7]
              x = self.avg_pool3d(x)
          else:
              # [N * num_segs, in_channels, 7, 7]
              x = self.avg_pool2d(x)
              # [N * num_segs, in_channels, 1, 1]
              x = x.reshape((-1, num_segs) + x.shape[1:])
              # [N, num_segs, in_channels, 1, 1]
              x = self.consensus(x)
              # [N, 1, in_channels, 1, 1]
              x = x.squeeze(1)
              # [N, in_channels, 1, 1]
          if self.dropout is not None:
              x = self.dropout(x)
              # [N, in_channels, 1, 1]
          x = x.view(x.size(0), -1)
          # [N, in_channels]
          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
  