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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 _extract_features_with_missing(self, dataset, tree_id = 0, missing_value_action = 'auto'):
 """ Extract features along with all the missing features associated with a dataset. Parameters dataset: bool Dataset on which to make predictions. missing_value_action: str, optional Action to perform when missing values are encountered. This can be one of: - 'auto': Choose a model dependent missing value policy. - 'impute': Proceed with evaluation by filling in the missing values with the mean of the training data. Missing values are also imputed if an entire column of data is missing during evaluation. - 'none': Treat missing value as is. Model must be able to handle missing value. - 'error' : Do not proceed with prediction and terminate with an error message. Returns ------- out : SFrame A table with two columns: - leaf_id : Leaf id of the corresponding tree. - missing_features : A list of missing feature, index pairs """


        # Extract the features from only one tree.
        sf = dataset
        sf['leaf_id'] = self.extract_features(dataset, missing_value_action)\
                             .vector_slice(tree_id)\
                             .astype(int)

        tree = self._get_tree(tree_id)
        type_map = dict(zip(dataset.column_names(), dataset.column_types()))

        def get_missing_features(row):
            x = row['leaf_id']
            path = tree.get_prediction_path(x)
            missing_id = [] # List of "missing_id" children.

            # For each node in the prediction path.
            for p in path:
                fname = p['feature']
                idx = p['index']
                f = row[fname]
                if type_map[fname] in [int, float]:
                    if f is None:
                        missing_id.append(p['child_id'])

                elif type_map[fname] in [dict]:
                    if f is None:
                        missing_id.append(p['child_id'])

                    if idx not in f:
                        missing_id.append(p['child_id'])
                else:
                    pass
            return missing_id

        sf['missing_id'] = sf.apply(get_missing_features, list)
        return sf[['leaf_id', 'missing_id']]