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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 keep_retrain(nkeep, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state):
 """ The model is retrained for each test sample with the non-important features set to a constant. If you want to know how important a set of features is you can ask how the model would be different if only those features had existed. To determine this we can mask the other features across the entire training and test datasets, then retrain the model. If we apply compare the output of this retrained model to the original model we can see the effect produced by only knowning the important features. Since for individualized explanation methods each test sample has a different set of most important features we need to retrain the model for every test sample to get the change in model performance when a specified fraction of the most important features are retained. """


    warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!")

    # see if we match the last cached call
    global _keep_cache
    args = (X_train, y_train, X_test, y_test, model_generator, metric)
    cache_match = False
    if "args" in _keep_cache:
        if all(a is b for a,b in zip(_keep_cache["args"], args)) and np.all(_keep_cache["attr_test"] == attr_test):
            cache_match = True

    X_train, X_test = to_array(X_train, X_test)

    # how many features to mask
    assert X_train.shape[1] == X_test.shape[1]

    # this is the model we will retrain many times
    model_masked = model_generator()

    # keep nkeep top features and re-train the model for each test explanation
    X_train_tmp = np.zeros(X_train.shape)
    X_test_tmp = np.zeros(X_test.shape)
    yp_masked_test = np.zeros(y_test.shape)
    tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6
    last_nkeep = _keep_cache.get("nkeep", None)
    last_yp_masked_test = _keep_cache.get("yp_masked_test", None)
    for i in tqdm(range(len(y_test)), "Retraining for the 'keep' metric"):
        if cache_match and last_nkeep[i] == nkeep[i]:
            yp_masked_test[i] = last_yp_masked_test[i]
        elif nkeep[i] == attr_test.shape[1]:
            yp_masked_test[i] = trained_model.predict(X_test[i:i+1])[0]
        else:

            # mask out the most important features for this test instance
            X_train_tmp[:] = X_train
            X_test_tmp[:] = X_test
            ordering = np.argsort(-attr_test[i,:] + tie_breaking_noise)
            X_train_tmp[:,ordering[nkeep[i]:]] = X_train[:,ordering[nkeep[i]:]].mean()
            X_test_tmp[i,ordering[nkeep[i]:]] = X_train[:,ordering[nkeep[i]:]].mean()

            # retrain the model and make a prediction
            model_masked.fit(X_train_tmp, y_train)
            yp_masked_test[i] = model_masked.predict(X_test_tmp[i:i+1])[0]

    # save our results so the next call to us can be faster when there is redundancy
    _keep_cache["nkeep"] = nkeep
    _keep_cache["yp_masked_test"] = yp_masked_test
    _keep_cache["attr_test"] = attr_test
    _keep_cache["args"] = args

    return metric(y_test, yp_masked_test)