code: def fit_predict(self, features, target, sample_weight=None, groups=None):
          
          self.fit(features, target, sample_weight=sample_weight, groups=groups)
  
          return self.predict(features)
docs: 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