content: import torch
  import torch.nn as nn
  
  from ..registry import HEADS
  from .base import BaseHead
  from ..builder import build_loss
  from .bnn import BayesianPredictor, get_uncertainty
  
  
  @HEADS.register_module()
  class SlowFastBNNHead(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='BayesianNNLoss'),
                   spatial_type='avg',
                   dropout_ratio=0.8,
                   init_std=0.01,
                   compute_uncertainty=False,
                   **kwargs):
  
          super().__init__(num_classes, in_channels, loss_cls, **kwargs)
          self.bnn_loss = self.loss_cls
          self.spatial_type = spatial_type
          self.dropout_ratio = dropout_ratio
          self.init_std = init_std
          self.compute_uncertainty = compute_uncertainty
  
          if self.dropout_ratio != 0:
              self.dropout = nn.Dropout(p=self.dropout_ratio)
          else:
              self.dropout = None
          # self.fc_cls = nn.Linear(in_channels, num_classes)
          self.bnn_cls = BayesianPredictor(self.in_channels, self.num_classes)
  
          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."""
          pass  # BNN do not need to explicity initialized
  
      def forward(self, x, npass=2, testing=False):
          """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]
          outputs, log_priors, log_variational_posteriors = self.bnn_cls(x, npass=npass, testing=testing)
  
          # gather output dictionary
          output = outputs.mean(0)
          log_prior = log_priors.mean()
          log_variational_posterior = log_variational_posteriors.mean()
          output_dict = {'pred_mean': output,
                         'log_prior': log_prior,
                         'log_posterior': log_variational_posterior}
          if self.compute_uncertainty:
              uncertain_alea, uncertain_epis = get_uncertainty(outputs)
              output_dict.update({'aleatoric': uncertain_alea,
                                  'epistemic': uncertain_epis})
  
          return output_dict
  