src/transformers/models/glpn/modeling_glpn.py/GLPNSelectiveFeatureFusion/forward
class GLPNSelectiveFeatureFusion:    def forward(self, local_features, global_features):
        # concatenate features along the channel dimension
        features = torch.cat((local_features, global_features), dim=1)
        # pass through convolutional layers
        features = self.convolutional_layer1(features)
        features = self.convolutional_layer2(features)
        features = self.convolutional_layer3(features)
        # apply sigmoid to get two-channel attention map
        attn = self.sigmoid(features)
        # construct hybrid features by adding element-wise
        hybrid_features = local_features * attn[:, 0, :, :].unsqueeze(1) + global_features * attn[
            :, 1, :, :
        ].unsqueeze(1)

        return hybrid_features