content: import torch.nn as nn
  from mmcv.cnn import normal_init
  
  from ..registry import HEADS
  from .base import AvgConsensus, BaseHead
  
  
  @HEADS.register_module()
  class TSNHead(BaseHead):
      """Class head for TSN.
  
      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.
          kwargs (dict, optional): Any keyword argument to be used to initialize
              the head.
      """
  
      def __init__(self,
                   num_classes,
                   in_channels,
                   loss_cls=dict(type='CrossEntropyLoss'),
                   spatial_type='avg',
                   consensus=dict(type='AvgConsensus', dim=1),
                   dropout_ratio=0.4,
                   init_std=0.01,
                   **kwargs):
          super().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs)
  
          self.spatial_type = spatial_type
          self.dropout_ratio = dropout_ratio
          self.init_std = init_std
  
          consensus_ = consensus.copy()
  
          consensus_type = consensus_.pop('type')
          if consensus_type == 'AvgConsensus':
              self.consensus = AvgConsensus(**consensus_)
          else:
              self.consensus = None
  
          if self.spatial_type == 'avg':
              # use `nn.AdaptiveAvgPool2d` to adaptively match the in_channels.
              self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
          else:
              self.avg_pool = None
  
          if self.dropout_ratio != 0:
              self.dropout = nn.Dropout(p=self.dropout_ratio)
          else:
              self.dropout = None
          self.fc_cls = nn.Linear(self.in_channels, self.num_classes)
  
      def init_weights(self):
          """Initiate the parameters from scratch."""
          normal_init(self.fc_cls, std=self.init_std)
  
      def forward(self, x, num_segs):
          """Defines the computation performed at every call.
  
          Args:
              x (torch.Tensor): The input data.
              num_segs (int): Number of segments into which a video
                  is divided.
          Returns:
              torch.Tensor: The classification scores for input samples.
          """
          # [N * num_segs, in_channels, 7, 7]
          if self.avg_pool is not None:
              x = self.avg_pool(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
  