[ENH] Add BORF transformer

[fspinna]

What does this implement/fix? Explain your changes.

I am trying to add a Bag-Of-Receptive-Fields transformer as seen in Spinnato, Francesco, et al. "A Bag of Receptive Fields for Time Series Extrinsic Predictions." arXiv preprint arXiv:2311.18029 (2023). It is basically a Bag-Of-Pattern transform, where a time series is transformed in a sparse matrix of counts of discretized subsequences.

Does your contribution introduce a new dependency? If yes, which one?

This contribution introduces a dependency to the pydata sparse library (https://sparse.pydata.org/en/stable/).

Any other comments?

I have a couple of issues/doubts:

1. the transformer fails 2 tests:

'test_persistence_via_pickle(estimator=BORF(),datatype=EqualLengthUnivariate-Classification-numpy3D)': "FAILED: 'csr_matrix' object has no attribute 'ravel'",
'check_fit_deterministic(estimator=BORF(),datatype=EqualLengthUnivariate-Classification-numpy3D)': "FAILED: 'csr_matrix' object has no attribute 'ravel'"

This is because the output of the transformer is a sparse scipy matrix (https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html#scipy.sparse.csr_matrix). I don't know if this is a problem. In practice, the sparse matrix is compatible with downstream sklearn estimators/transformers.

2. the sparse package is used as a soft dependency, which is imported in the transform of IndividualBORF, which inherits from sklearn's BaseEstimator, TransformerMixin, and not BaseCollectionTransformer. Is this a problem?

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