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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 confusion_matrix(targets, predictions):
 r""" Compute the confusion matrix for classifier predictions. Parameters targets : SArray Ground truth class labels (cannot be of type float). predictions : SArray The prediction that corresponds to each target value. This vector must have the same length as ``targets``. The predictions SArray cannot be of type float. Returns ------- out : SFrame An SFrame containing counts for 'target_label', 'predicted_label' and 'count' corresponding to each pair of true and predicted labels. See Also -------- accuracy Examples -------- """


    _supervised_evaluation_error_checking(targets, predictions)
    _check_same_type_not_float(targets, predictions)
    return _turicreate.extensions._supervised_streaming_evaluator(targets,
                       predictions, "confusion_matrix_no_map", {})