feat: Policy driven training control

Signed-off-by: Padmanabha V Seshadri <seshapad@in.ibm.com>

examples/training-control-definitions/Readme.md

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+# Note on training control definition examples
+- `ctldef_step_v0.3.yaml`: Defines a training controller which computes loss at every step and loss consistently increases for three steps, then the training is stopped.
+- `ctldef_epoch_v0.3.yaml`: Defines a epoch level training controller which computes loss at every epoch. The rule applied here is to compare a current epoch loss with previous epoch loss and it turns out to be more, then training is stopped.
+- `ctldef_epoch_threshold_v0.3.yaml`: Defines a training controller similar to previous case, but also adds a threshold constraint.
+- `ctldef_evaluate_v0.3.yaml`: Defines a training controller which behaves similar to the `EarlyStoppingCallback` from hugging face which can be found [here](https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/trainer_callback.py#L543).