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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def fit_transform(self, X, **kwargs):
 """Computes the diffusion operator and the position of the cells in the embedding space Parameters X : array, shape=[n_samples, n_features] input data with `n_samples` samples and `n_dimensions` dimensions. Accepted data types: `numpy.ndarray`, `scipy.sparse.spmatrix`, `pd.DataFrame`, `anndata.AnnData` If `knn_dist` is 'precomputed', `data` should be a n_samples x n_samples distance or affinity matrix kwargs : further arguments for `PHATE.transform()` Keyword arguments as specified in :func:`~phate.PHATE.transform` Returns ------- embedding : array, shape=[n_samples, n_dimensions] The cells embedded in a lower dimensional space using PHATE """

        tasklogger.log_start('PHATE')
        self.fit(X)
        embedding = self.transform(**kwargs)
        tasklogger.log_complete('PHATE')
        return embedding