content: import torch.nn as nn
  
  from ..registry import HEADS
  from .tsn_head import TSNHead
  
  
  @HEADS.register_module()
  class TPNHead(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, *args, **kwargs):
          super().__init__(*args, **kwargs)
  
          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 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]
          cls_score = self.fc_cls(x)
          # [N, num_classes]
          return cls_score
  