def forward(self, x: Tuple[torch.Tensor]) -> Tuple[torch.Tensor]:
        """Forward function.

        Args:
            x (Tuple[torch.Tensor]): The feature map of backbone.

        Returns:
            Tuple[torch.Tensor]: The output features.
        """
        assert len(x) == 1
        x = x[0]
        if self.with_avg_pool:
            x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc0(x)
        x = self.bn0(x)
        for fc_name, bn_name in zip(self.fc_names, self.bn_names):
            fc = getattr(self, fc_name)
            x = self.relu(x)
            x = fc(x)
            if bn_name is not None:
                bn = getattr(self, bn_name)
                x = bn(x)
        return (x, )
Forward function.

        Args:
            x (Tuple[torch.Tensor]): The feature map of backbone.

        Returns:
            Tuple[torch.Tensor]: The output features.
        
mmpretrain/models/necks/nonlinear_neck.py