chore: import upstream snapshot with attribution

examples/python/ml_depth_pro/README.md

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+<!--[metadata]
+title = "DepthPro"
+tags = ["2D", "3D", "HuggingFace", "Pinhole camera", "Depth"]
+source = "https://github.com/rerun-io/hf-example-ml-depth-pro"
+thumbnail = "https://static.rerun.io/ml_depth_pro/e29c5afc5e4d4a36656abe0e4559a952a5a2fa68/480w.png"
+thumbnail_dimensions = [480, 294]
+-->
+
+This example visualizes the paper "Depth Pro: Sharp Monocular Metric Depth in Less Than a Second" ([arXiv](https://arxiv.org/abs/2410.02073)).
+The example runs inference for each frame in the provided video, and logs the predicted depth map to Rerun.
+
+## Background
+
+DepthPro is a fast, zero-shot monocular depth estimation model developed by Apple.
+It produces highly detailed and sharp depth maps at 2.25 megapixels in just 0.3 seconds on a standard GPU.
+The model works using a multi-scale vision transformer architecture that captures both global context and fine-grained details, enabling it to
+accurately predict metric depth _without_ requiring camera intrinsics such as focal length or principal point.
+Additionally the model is able to predict the focal length of camera used to take the photo, which is also visualized in this example.
+
+This example uses the open-source code and [model weights](https://huggingface.co/apple/DepthPro) provided by the authors.
+
+## Run the code
+
+This is an external example. Check the [repository](https://github.com/rerun-io/hf-example-ml-depth-pro) for more information.
+
+You can try the example on a HuggingFace space [here](https://huggingface.co/spaces/oxkitsune/rerun-ml-depth-pro).