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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 predict(model, x):
 """ Predict with a scikit learn model Parameters model : scikit learn classifier x : dask Array See docstring for ``da.learn.fit`` """

    if not hasattr(x, "chunks") and hasattr(x, "to_dask_array"):
        x = x.to_dask_array()
    assert x.ndim == 2
    if len(x.chunks[1]) > 1:
        x = x.rechunk(chunks=(x.chunks[0], sum(x.chunks[1])))
    func = partial(_predict, model)
    xx = np.zeros((1, x.shape[1]), dtype=x.dtype)
    dt = model.predict(xx).dtype
    return x.map_blocks(func, chunks=(x.chunks[0], (1,)), dtype=dt).squeeze()