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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 analyze(data, normalize=None, reduce=None, ndims=None, align=None, internal=False):
 """ Wrapper function for normalize -> reduce -> align transformations. Parameters data : numpy array, pandas df, or list of arrays/dfs The data to analyze normalize : str or False or None If set to 'across', the columns of the input data will be z-scored across lists (default). That is, the z-scores will be computed with with respect to column n across all arrays passed in the list. If set to 'within', the columns will be z-scored within each list that is passed. If set to 'row', each row of the input data will be z-scored. If set to False, the input data will be returned with no z-scoring. reduce : str or dict Decomposition/manifold learning model to use. Models supported: PCA, IncrementalPCA, SparsePCA, MiniBatchSparsePCA, KernelPCA, FastICA, FactorAnalysis, TruncatedSVD, DictionaryLearning, MiniBatchDictionaryLearning, TSNE, Isomap, SpectralEmbedding, LocallyLinearEmbedding, and MDS. Can be passed as a string, but for finer control of the model parameters, pass as a dictionary, e.g. reduce={'model' : 'PCA', 'params' : {'whiten' : True}}. See scikit-learn specific model docs for details on parameters supported for each model. ndims : int Number of dimensions to reduce align : str or dict If str, either 'hyper' or 'SRM'. If 'hyper', alignment algorithm will be hyperalignment. If 'SRM', alignment algorithm will be shared response model. You can also pass a dictionary for finer control, where the 'model' key is a string that specifies the model and the params key is a dictionary of parameter values (default : 'hyper'). Returns analyzed_data : list of numpy arrays The processed data """


    # return processed data
    return aligner(reducer(normalizer(data, normalize=normalize, internal=internal),
                   reduce=reduce, ndims=ndims, internal=internal), align=align)