feat: opt-in pipeline fit optimisations, native bucketize, and sampled fitting support with caching

[ConorWorthington]

Description

Provide a short description of the PR changes.

The below checklists come from the docs page on adding new transformers here

Keras Layer Checklist

Verify that:

• The new Keras layer extends BaseLayer
• The _call method has been implemented in the new layer.
• The compatible_dtypes property is defined in the new layer.
• The new layer is decorated with @tf.keras.utils.register_keras_serializable(package=kamae.__name__).
• The new layer takes a name, input_dtype, and output_dtype as arguments to the constructor and that this is passed to the super constructor.
• The Keras layer is serializable. I have implemented the get_config method.
• There are unit tests of the new layer.
• There is a specific test of layer serialisation added here.
• The new layer is imported in the init.py file in the layers directory.

Spark Transformer/Estimator Checklist

Verify that:

• The new Spark Transformer extends BaseTransformer.
• If the new transform needs a fit method, a Spark Estimator has been implemented that extends BaseEstimator.
• The instructions in the above docs page have been followed for the __init__ and setParams methods.
• The transformer uses one of the input/output mixin classes from base.py.
• If the new transformer requires more parameters that would need to be serialised to the Spark ML pipeline, there is a implemented parameter class by extending the Params class here.
• The compatible_dtypes property has been implemented to specify the input/output data types that my transformer/estimator supports.
• A Keras subclassed layer is returned in the transformer's get_tf_layer method.
• There are unit tests of the new transform. In particular, there are parity tests between the Spark and Keras implementations.
• The new transformer/estimator is imported in the init.py file in the transformers/estimators directory.

Finally, please verify that:

• There is a new entry (alphabetical order) in the README table describing the new layer/transformer