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

        Args:
            x (List[torch.Tensor]): list of feature maps, len(x) according to
                len(num_crops).

        Returns:
            torch.Tensor: The projection vectors.
        """
        avg_out = []
        for _x in x:
            _x = _x[0]
            if self.with_avg_pool:
                _out = self.avgpool(_x)
                avg_out.append(_out)
        feat_vec = torch.cat(avg_out)  # [sum(num_crops) * N, C]
        feat_vec = feat_vec.view(feat_vec.size(0), -1)
        output = self.forward_projection(feat_vec)
        return output
Forward function.

        Args:
            x (List[torch.Tensor]): list of feature maps, len(x) according to
                len(num_crops).

        Returns:
            torch.Tensor: The projection vectors.
        
mmpretrain/models/necks/swav_neck.py