Traceback (most recent call last):
  File "/media1/data/anna/script_facemap_full.py", line 61, in <module>
    main()
  File "/media1/data/anna/script_facemap_full.py", line 45, in main
    deeplabcut.train_network(config, shuffle=shuffle) #step 2 - to edit training parameters such as max epochs to run, edit thepytorch_config.yaml file under dlc-models
  File "/media1/data/anna/DeepLabCut/deeplabcut/compat.py", line 245, in train_network
    return train_network(
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/apis/train.py", line 339, in train_network
    train(
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/apis/train.py", line 189, in train
    runner.fit(
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/train.py", line 177, in fit
    valid_loss = self._epoch(
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/train.py", line 217, in _epoch
    losses_dict = self.step(batch, mode)
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/train.py", line 321, in step
    self.logger.log_images(batch, outputs, target, step=self.current_epoch)
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/logger.py", line 326, in log_images
    images = self._prepare_images(inputs, outputs, targets)
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/logger.py", line 227, in _prepare_images
    image_logs[f"{base}.input"] = self._prepare_image(
  File "/media1/data/anna/DeepLabCut/deeplabcut/pose_estimation_pytorch/runners/logger.py", line 198, in _prepare_image
    image = draw_keypoints(
  File "/media1/data/anna/miniconda/envs/dlc/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/media1/data/anna/miniconda/envs/dlc/lib/python3.10/site-packages/torchvision/utils.py", line 427, in draw_keypoints
    draw.ellipse([x1, y1, x2, y2], fill=colors, outline=None, width=0)
  File "/media1/data/anna/miniconda/envs/dlc/lib/python3.10/site-packages/PIL/ImageDraw.py", line 223, in ellipse
    self.draw.draw_ellipse(xy, fill_ink, 1)
ValueError: x1 must be greater than or equal to x0

def _prepare_image(
    self,
    image: torch.Tensor,
    denormalize: bool = False,
    keypoints: torch.Tensor | None = None,
    bboxes: torch.Tensor | None = None,
) -> np.ndarray:
    """
    Args:
        image: the image to log, of shape (C, H, W), of any data type
        denormalize: whether to remove ImageNet channel normalization
        keypoints: size (num_instances, K, 2) the K keypoints location
        bboxes: size (N, 4) containing bboxes in (xmin, ymin, xmax, ymax)

    Returns:
        an uint8 array with keypoints and bounding boxes drawn
    """
    if denormalize:
        image = self._denormalize(image.unsqueeze(0)).squeeze()

    image = F.convert_image_dtype(image.detach().cpu(), dtype=torch.uint8)
    if keypoints is not None and len(keypoints) > 0:
        assert len(keypoints.shape) == 3
        #keypoints[keypoints < 0] = np.nan
        keypoints[torch.isnan(keypoints).any(dim=2)] = 0

        print('what we put into draw keypoints ', keypoints[..., :2]) 
        image = draw_keypoints(
            image, keypoints=keypoints[..., :2], colors="red", radius=5
        )

    if bboxes is not None and len(bboxes) > 0:
        assert len(bboxes.shape) == 2
        image = draw_bounding_boxes(image, boxes=bboxes[:, :4], width=1)

    return image.permute(1, 2, 0).numpy()