Introduce code from Rust monorepo. This is a mess

tts/NeMo/examples/cv/mnist_lenet5_image_classification_pure_lightning.py

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+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+# from hydra.utils import instantiate
+import pytorch_lightning as ptl
+from omegaconf import DictConfig, OmegaConf
+
+from nemo.collections.cv.models import MNISTLeNet5, MNISTLeNet5Config
+from nemo.core.config import Config, TrainerConfig, hydra_runner
+from nemo.utils import logging
+
+
+@dataclass
+class AppConfig(Config):
+    """
+    This is structured config for this application.
+
+    Args:
+        name: Description of the application.
+        trainer: configuration of the trainer.
+        model: configuation of the model.
+    """
+
+    name: str = "Training of a LeNet-5 Model using a pure PyTorchLightning approach - using DDP on 2 GPUs."
+    trainer: TrainerConfig = TrainerConfig(gpus=2, accelerator="ddp")
+    model: MNISTLeNet5Config = MNISTLeNet5Config()
+
+
+@hydra_runner(schema=AppConfig, config_name="AppConfig")
+def main(cfg: DictConfig):
+    # Show configuration - user can modify every parameter from command line!
+    logging.info("Application config\n" + OmegaConf.to_yaml(cfg))
+
+    # The "model" - with dataloader/dataset inside of it.
+    lenet5 = MNISTLeNet5(cfg.model)
+
+    # Setup train data loader and optimizer
+    lenet5.setup_training_data()
+
+    # Setup optimizer and scheduler
+    lenet5.setup_optimization()
+
+    # Create trainer.
+    trainer = ptl.Trainer(**(cfg.trainer))
+    # trainer = instantiate(cfg.trainer)
+
+    # Train.
+    trainer.fit(model=lenet5)
+
+
+if __name__ == "__main__":
+    main()  # TODO: No cfg in function call, and no hydra runner