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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 fit_predict(self, features, target, sample_weight=None, groups=None):
 """Call fit and predict in sequence. Parameters features: array-like {n_samples, n_features} Feature matrix target: array-like {n_samples} List of class labels for prediction sample_weight: array-like {n_samples}, optional Per-sample weights. Higher weights force TPOT to put more emphasis on those points groups: array-like, with shape {n_samples, }, optional Group labels for the samples used when performing cross-validation. This parameter should only be used in conjunction with sklearn's Group cross-validation functions, such as sklearn.model_selection.GroupKFold Returns array-like: {n_samples} Predicted target for the provided features """

        self.fit(features, target, sample_weight=sample_weight, groups=groups)

        return self.predict(features)