def METHOD_NAME(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    confidences = []
    locations = []
    start_layer_index = 0
    header_index = 0
    features = []
    for end_layer_index in self.source_layer_indexes:
        if isinstance(end_layer_index, tuple):
            added_layer = end_layer_index[1]
            end_layer_index = end_layer_index[0]
        else:
            added_layer = None
        for layer in self.base_net[start_layer_index: end_layer_index]:
            x = layer(x)
        start_layer_index = end_layer_index
        if added_layer:
            y = added_layer(x)
        else:
            y = x
        #confidence, location = self.compute_header(header_index, y)
        features.append(y)
        header_index += 1
        # confidences.append(confidence)
        # locations.append(location)
    for layer in self.base_net[end_layer_index:]:
        x = layer(x)
    for layer in self.extras:
        x = layer(x)
        #confidence, location = self.compute_header(header_index, x)
        features.append(x)
        header_index += 1
        # confidences.append(confidence)
        # locations.append(location)
    upstream_feature = None
    for i in range(len(features) - 1, -1, -1):
        feature = features[i]
        if upstream_feature is not None:
            upstream_feature = self.upsamplers[i](upstream_feature)
            upstream_feature += feature
        else:
            upstream_feature = feature
        confidence, location = self.compute_header(i, upstream_feature)
        confidences.append(confidence)
        locations.append(location)
    confidences = torch.cat(confidences, 1)
    locations = torch.cat(locations, 1)
    return confidences, locations