Lpd

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* Added Predictor class !! predicting was never easier, see README for more details
* Added example for train/save/load/predict using the new Predictor
* Added unittests for predictor

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* Added trainer validation for metric_name_to_func
* ModelCheckpoint args changed to accept CallbackMonitor
* EarlyStopping args changed to accept CallbackMonitor
* Adjusted examples and tests
* Added more unittests

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* Added test for trainer save and load
* Moved LossOptimizerHandlerBase validation to Trainer.train() instead if Trainer.__init__
* Added name property to CallbackMonitorResult
* StatsPrint now accept list of monitors arguments for metrics
* StatsPrint will make validations on provided monitors
* Removed num_epochs from Trainer arguments, now its in Trainer.train(num_epochs)
* Adjusted all examples

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* Added predict_sample and predict_data_loader methods to Trainer
* Added LossOptimizerHandler and LossOptimizerHandlerBase to callbacks
* Trainer must have at least one callback of type LossOptimizerHandlerBase
* Removed optimizer_step_and_zero_grad_criteria argument from Trainer, use LossOptimizerHandler callback instead
* CallbackContext now have optimizer, scheduler and train_last_loss properties for easier access
* CollectOutput arguments now must be passed explicitly
* CallbackBase will raise exception if __call__ not implemented
* Trainer have callbacks validation upon initialization, more validations will be added
* SchedulerStep scheduler_parameters_func should accept CallbackContext instead of Trainer
* Added copy_model_weights to torch_utils, (as requested, thank you for using lpd :))
* Adjusted all examples

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* Added predict_batch
* Adjusted predict example
* Added threshold argument to BinaryAccuracy and BinaryAccuracyWithLogits

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* Custom metrics are now in a separate module
* Custom metrics now classes instead of functions
* Added unittests for metrics