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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 correct(datasets_full, genes_list, return_dimred=False, batch_size=BATCH_SIZE, verbose=VERBOSE, ds_names=None, dimred=DIMRED, approx=APPROX, sigma=SIGMA, alpha=ALPHA, knn=KNN, return_dense=False, hvg=None, union=False, geosketch=False, geosketch_max=20000):
 """Integrate and batch correct a list of data sets. Parameters datasets_full : `list` of `scipy.sparse.csr_matrix` or of `numpy.ndarray` Data sets to integrate and correct. genes_list: `list` of `list` of `string` List of genes for each data set. return_dimred: `bool`, optional (default: `False`) In addition to returning batch corrected matrices, also returns integrated low-dimesional embeddings. batch_size: `int`, optional (default: `5000`) The batch size used in the alignment vector computation. Useful when correcting very large (>100k samples) data sets. Set to large value that runs within available memory. verbose: `bool` or `int`, optional (default: 2) When `True` or not equal to 0, prints logging output. ds_names: `list` of `string`, optional When `verbose=True`, reports data set names in logging output. dimred: `int`, optional (default: 100) Dimensionality of integrated embedding. approx: `bool`, optional (default: `True`) Use approximate nearest neighbors, greatly speeds up matching runtime. sigma: `float`, optional (default: 15) Correction smoothing parameter on Gaussian kernel. alpha: `float`, optional (default: 0.10) Alignment score minimum cutoff. knn: `int`, optional (default: 20) Number of nearest neighbors to use for matching. return_dense: `bool`, optional (default: `False`) Return `numpy.ndarray` matrices instead of `scipy.sparse.csr_matrix`. hvg: `int`, optional (default: None) Use this number of top highly variable genes based on dispersion. Returns ------- corrected, genes By default (`return_dimred=False`), returns a two-tuple containing a list of `scipy.sparse.csr_matrix` each with batch corrected values, and a single list of genes containing the intersection of inputted genes. integrated, corrected, genes When `return_dimred=False`, returns a three-tuple containing a list of `numpy.ndarray` with integrated low dimensional embeddings, a list of `scipy.sparse.csr_matrix` each with batch corrected values, and a a single list of genes containing the intersection of inputted genes. """

    datasets_full = check_datasets(datasets_full)
    
    datasets, genes = merge_datasets(datasets_full, genes_list,
                                     ds_names=ds_names, union=union)
    datasets_dimred, genes = process_data(datasets, genes, hvg=hvg,
                                          dimred=dimred)
    
    datasets_dimred = assemble(
        datasets_dimred, # Assemble in low dimensional space.
        expr_datasets=datasets, # Modified in place.
        verbose=verbose, knn=knn, sigma=sigma, approx=approx,
        alpha=alpha, ds_names=ds_names, batch_size=batch_size,
        geosketch=geosketch, geosketch_max=geosketch_max,
    )

    if return_dense:
        datasets = [ ds.toarray() for ds in datasets ]

    if return_dimred:
        return datasets_dimred, datasets, genes

    return datasets, genes