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Computational Statistics and Machine Learning unit @IIT 6

• "__main__.MLPDecoder",
• "lightning.pytorch.trainer.connectors.logger_connector.result._ResultMetric",
• "lightning.pytorch.trainer.states.RunningStage",
• "numpy.ndarray",
• "numpy.core.numeric._frombuffer",
• "fsspec.spec.make_instance",
• "torch.nn.modules.activation.ReLU",
• "fsspec.implementations.local.LocalFileSystem",
• "lightning.pytorch.callbacks.model_summary.ModelSummary",
• "lightning.pytorch.plugins.precision.precision.Precision",
• "__main__.MLPEncoder",
• "lightning.pytorch.strategies.strategy._ForwardRedirection",
• "lightning.pytorch.trainer.connectors.signal_connector._SignalConnector",
• "lightning.pytorch.trainer.trainer.Trainer",
• "torch.utils.data.sampler.RandomSampler",
• "kooplearn.nn.data.collate_context_dataset",
• "lightning.pytorch.trainer.connectors.checkpoint_connector._CheckpointConnector",
• "torch.nn.modules.container.Sequential",
• "lightning.pytorch.trainer.connectors.data_connector._DataHookSelector",
• "lightning.pytorch.loops.progress._StartedTracker",
• "lightning.pytorch.loops.prediction_loop._PredictionLoop",
• "lightning.pytorch.loops.progress._SchedulerProgress",
• "numpy.dtype",
• "lightning.pytorch.trainer.states.TrainerState",
• "exps.lorenz63.cae.ConsistentAE",
• "torch.utils.data.sampler.BatchSampler",
• "lightning.pytorch.strategies.single_device.SingleDeviceStrategy",
• "lightning.pytorch.utilities.model_helpers._ModuleMode",
• "lightning.pytorch.loops.progress._ReadyCompletedTracker",
• "lightning.pytorch.loops.optimization.automatic._AutomaticOptimization",
• "builtins.int",
• "lightning.pytorch.loggers.wandb.WandbLogger",
• "torch._utils._rebuild_tensor_v2",
• "lightning.pytorch.profilers.base.PassThroughProfiler",
• "kooplearn.data.TensorContextDataset",
• "linear_operator_learning.nn.modules.mlp.MLP",
• "src.utils.EpochTimerCallback",
• "lightning.pytorch.accelerators.cuda.CUDAAccelerator",
• "torch.storage._load_from_bytes",
• "torchmetrics.metric.jit_distributed_available",
• "lightning.pytorch.trainer.connectors.logger_connector.result._ResultCollection",
• "lightning_utilities.core.rank_zero.WarningCache",
• "torch.nn.modules.dropout.Dropout",
• "lightning.pytorch.trainer.connectors.logger_connector.logger_connector._LoggerConnector",
• "lightning.pytorch.trainer.connectors.data_connector._DataLoaderSource",
• "lightning.fabric.utilities.data.AttributeDict",
• "lightning.pytorch.loops.progress._OptimizationProgress",
• "lightning.pytorch.trainer.connectors.logger_connector.result._Sync.no_op",
• "lightning.pytorch.loops.evaluation_loop._EvaluationLoop",
• "builtins.getattr",
• "lightning.pytorch.callbacks.model_checkpoint.ModelCheckpoint",
• "torch.optim.adam.Adam",
• "torch.nn.modules.linear.Linear",
• "lightning.pytorch.loops.fit_loop._FitLoop",
• "lightning.pytorch.loops.training_epoch_loop._TrainingEpochLoop",
• "lightning.pytorch.loops.progress._ProcessedTracker",
• "kooplearn.data.TrajectoryContextDataset",
• "lightning.pytorch.trainer.connectors.data_connector._DataConnector",
• "lightning.pytorch.loops.progress._OptimizerProgress",
• "numpy.core.multiarray._reconstruct",
• "torch.utils.data.dataloader.DataLoader",
• "torch.device",
• "lightning.pytorch.trainer.states.TrainerFn",
• "lightning.pytorch.utilities.combined_loader.CombinedLoader",
• "lightning.fabric.plugins.io.torch_io.TorchCheckpointIO",
• "lightning.pytorch.callbacks.progress.tqdm_progress.TQDMProgressBar",
• "linear_operator_learning.nn.modules.mlp._MLPBlock",
• "functools.partial",
• "lightning.pytorch.trainer.states.TrainerStatus",
• "lightning.pytorch.loops.optimization.manual._ManualOptimization",
• "torch._utils._rebuild_parameter",
• "torch.utils._pytree.LeafSpec",
• "collections.OrderedDict",
• "builtins.dict",
• "lightning.pytorch.loops.progress._BatchProgress",
• "lightning.fabric.plugins.environments.lightning.LightningEnvironment",
• "torch._C._VariableFunctionsClass",
• "lightning.pytorch.trainer.connectors.logger_connector.result._Sync",
• "collections.defaultdict",
• "lightning.pytorch.trainer.connectors.logger_connector.result._Metadata",
• "lightning.pytorch.trainer.connectors.accelerator_connector._AcceleratorConnector",
• "torch.float32",
• "lightning.pytorch.loops.progress._Progress",
• "lightning.pytorch.trainer.connectors.callback_connector._CallbackConnector",
• "exps.lorenz63.cae.ConsistentAEModule"

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