chore: import upstream snapshot with attribution

examples/pytorch/HPOExample/README.md

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+# PyTorch Hyperparameter Optimization Example
+
+This example demonstrates hyperparameter optimization with MLflow tracking using pure PyTorch (no Lightning dependencies).
+
+## What it demonstrates
+
+- **MLflow nested runs**: Parent run tracks the overall HPO experiment, child runs track individual trials
+- **Hyperparameter tuning**: Uses Optuna to optimize learning rate, hidden layer size, dropout rate, and batch size
+- **Pure PyTorch**: Simple, clean implementation without framework overhead
+- **Fast training**: MNIST classification completes quickly for rapid iteration
+
+## Architecture
+
+The model is a simple 2-layer neural network:
+
+```
+Input (784) → FC1 (hidden_size) → ReLU → Dropout → FC2 (10) → LogSoftmax
+```
+
+## Hyperparameters optimized
+
+- `lr`: Learning rate (1e-4 to 1e-1, log scale)
+- `hidden_size`: Hidden layer size (64 to 512, step 64)
+- `dropout_rate`: Dropout probability (0.1 to 0.5)
+- `batch_size`: Batch size (32, 64, or 128)
+
+## Running the example
+
+### Quick test (3 trials, 3 epochs each)
+
+```bash
+python hpo_mnist.py --n-trials 3 --max-epochs 3
+```
+
+### Full optimization (10 trials, 5 epochs each)
+
+```bash
+python hpo_mnist.py --n-trials 10 --max-epochs 5
+```
+
+### Using MLflow projects
+
+```bash
+mlflow run . -P n_trials=5 -P max_epochs=3
+```
+
+## Viewing results
+
+After running, view the results in MLflow UI:
+
+```bash
+mlflow server
+```
+
+Navigate to http://localhost:5000 to see:
+
+- Parent run with overall HPO results
+- Child runs for each trial with their hyperparameters and metrics
+- Comparison view to analyze which hyperparameters work best
+
+## Dependencies
+
+- `torch>=2.1`: PyTorch for model training
+- `torchvision>=0.15.1`: MNIST dataset
+- `optuna>=3.0.0`: Hyperparameter optimization framework
+- `mlflow`: Experiment tracking
+
+**No Lightning, no torchmetrics, no transformers** = no dependency conflicts! 🎉