the post-processed
    predictions as a ``List[Dict[Tensor]]``, one for each input image. The fields of the ``Dict`` are as
    follows, where ``N`` is the number of detected instances:

        - boxes (``FloatTensor[N, 4]``): the predicted boxes in ``[x1, y1, x2, y2]`` format, with
          ``0 <= x1 < x2 <= W`` and ``0 <= y1 < y2 <= H``.
        - labels (``Int64Tensor[N]``): the predicted labels for each instance
        - scores (``Tensor[N]``): the scores or each instance
        - keypoints (``FloatTensor[N, K, 3]``): the locations of the predicted keypoints, in ``[x, y, v]`` format.

    For more details on the output, you may refer to :ref:`instance_seg_output`.

    Keypoint R-CNN is exportable to ONNX for a fixed batch size with inputs images of fixed size.

    Example::

        >>> model = torchvision.models.detection.keypointrcnn_resnet50_fpn(weights=KeypointRCNN_ResNet50_FPN_Weights.DEFAULT)
        >>> model.eval()
        >>> x = [torch.rand(3, 300, 400), torch.rand(3, 500, 400)]
        >>> predictions = model(x)
        >>>
        >>> # optionally, if you want to export the model to ONNX:
        >>> torch.onnx.export(model, x, "keypoint_rcnn.onnx", opset_version = 11)

    Args:
        weights (:class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights`
            below for more details, and possible values. By default, no
            pre-trained weights are used.
        progress (bool): If True, displays a progress bar of the download to stderr
        num_classes (int, optional): number of output classes of the model (including the background)
        num_keypoints (int, optional): number of keypoints
        weights_backbone (:class:`~torchvision.models.ResNet50_Weights`, optional): The
            pretrained weights for the backbone.
        trainable_backbone_layers (int, optional): number of trainable (not frozen) layers starting from final block.
            Valid values are between 0 and 5, with 5 meaning all backbone layers are trainable. If ``None`` is
            passed (the default) this value is set to 3.

    .. autoclass:: torchvision.models.detection.KeypointRCNN_ResNet50_FPN_Weights
        :members:
    NrC