chore: import upstream snapshot with attribution

examples/python/dataloader/README.md

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+Train a [LeRobot](https://github.com/huggingface/lerobot) ACT policy using Rerun's experimental PyTorch dataloader, streaming trajectory data directly from a Rerun catalog.
+
+For an explanation of the dataloader API and how the example fits together, see the [Train PyTorch models with the Rerun dataloader](https://rerun.io/docs/howto/train) how-to guide.
+
+## Run the code
+
+### 1. Install dependencies
+
+This example has its own `uv` project, separate from the workspace `.venv`, because LeRobot requires
+Python >=3.12 while the workspace supports older versions.
+
+**Standalone** (sparse-checkout of just this directory, no local Rerun build):
+
+```bash
+uv sync --no-sources --no-dev
+```
+
+**Monorepo dev** (full repo checkout, editable local `rerun-sdk`):
+
+```bash
+cd examples/python/dataloader
+RERUN_ALLOW_MISSING_BIN=1 uv sync
+uv pip install ../../../rerun_py/rerun_dev_fixup
+```
+
+Then either `source .venv/bin/activate` or prefix subsequent commands with `uv run`.
+
+### 2. Start a local Rerun server
+
+In a separate terminal:
+
+```bash
+rerun server
+```
+
+This serves a Rerun server at `rerun+http://127.0.0.1:51234` (the default used by the scripts).
+
+### 3. Prepare and register the dataset
+
+Downloads a LeRobot dataset from HuggingFace, splits it into per-episode RRDs, and registers them as a dataset in the catalog:
+
+```bash
+uv run python prepare_dataset.py
+```
+
+Pass `--repo-id user/other_lerobot_ds` to use a different dataset, or `--catalog-url ""` to skip registration and only write local RRDs.
+
+### 4. Train
+
+```bash
+uv run python train.py
+```
+
+The script streams batches from the catalog, trains an ACT policy for a few epochs, and saves a checkpoint to `act_checkpoint/`.
+
+It accepts a few CLI flags (run `uv run python train.py --help` for the full list):
+
+```bash
+uv run python train.py \
+    --catalog-url rerun+http://127.0.0.1:51234 \
+    --dataset rerun_so101-pick-and-place \
+    --num-segments 3 \
+    --epochs 5 \
+    --batch-size 8 \
+    --num-workers 8 \
+    --lr 1e-5 \
+    --checkpoint-dir act_checkpoint \
+    --dataset-style iterable  # or "map"
+```
+
+Pass `--num-segments 0` to train on all segments in the dataset.
+
+### Training with traces
+
+```sh
+TELEMETRY_ENABLED=true OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4317 uv run python train.py
+```