How to use the econml.ortho_forest.DiscreteTreatmentOrthoForest function in econml

Returns
        -------
        self: an instance of self.
        """
        # Check that T is shape (n, )
        # Check T is numeric
        T = self._check_treatment(T)
        # Train label encoder
        T = self._label_encoder.fit_transform(T)
        self._one_hot_encoder = OneHotEncoder(sparse=False, categories='auto').fit(T.reshape(-1, 1))
        # Define number of classes
        self.n_T = self._label_encoder.classes_.shape[0]
        self.nuisance_estimator = DiscreteTreatmentOrthoForest.nuisance_estimator_generator(
            self.propensity_model, self.model_Y, self.n_T, self.random_state, second_stage=False)
        self.second_stage_nuisance_estimator = DiscreteTreatmentOrthoForest.nuisance_estimator_generator(
            self.propensity_model_final, self.model_Y_final, self.n_T, self.random_state, second_stage=True)
        self.transformer = FunctionTransformer(
            func=(lambda T:
                  self._one_hot_encoder.transform(
                      reshape(self._label_encoder.transform(T.ravel()), (-1, 1)))[:, 1:]),
            validate=False)
        # Call `fit` from parent class
        return super().fit(Y, T, X, W=W, inference=inference)

self.propensity_model = clone(propensity_model, safe=False)
        self.model_Y = clone(model_Y, safe=False)
        self.propensity_model_final = clone(propensity_model_final, safe=False)
        self.model_Y_final = clone(model_Y_final, safe=False)
        if self.propensity_model_final is None:
            self.propensity_model_final = clone(self.propensity_model, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Nuisance estimators shall be defined during fitting because they need to know the number of distinct
        # treatments
        nuisance_estimator = None
        second_stage_nuisance_estimator = None
        # Define parameter estimators
        parameter_estimator = DiscreteTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            DiscreteTreatmentOrthoForest.second_stage_parameter_estimator_gen(lambda_reg)
        # Define moment and mean gradient estimator
        moment_and_mean_gradient_estimator =\
            DiscreteTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        # Define autoencoder
        self._label_encoder = LabelEncoder()
        super(DiscreteTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,

random_state=None):
        # Copy and/or define models
        self.propensity_model = clone(propensity_model, safe=False)
        self.model_Y = clone(model_Y, safe=False)
        self.propensity_model_final = clone(propensity_model_final, safe=False)
        self.model_Y_final = clone(model_Y_final, safe=False)
        if self.propensity_model_final is None:
            self.propensity_model_final = clone(self.propensity_model, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Nuisance estimators shall be defined during fitting because they need to know the number of distinct
        # treatments
        nuisance_estimator = None
        second_stage_nuisance_estimator = None
        # Define parameter estimators
        parameter_estimator = DiscreteTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            DiscreteTreatmentOrthoForest.second_stage_parameter_estimator_gen(lambda_reg)
        # Define moment and mean gradient estimator
        moment_and_mean_gradient_estimator =\
            DiscreteTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        # Define autoencoder
        self._label_encoder = LabelEncoder()
        super(DiscreteTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,

if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Nuisance estimators shall be defined during fitting because they need to know the number of distinct
        # treatments
        nuisance_estimator = None
        second_stage_nuisance_estimator = None
        # Define parameter estimators
        parameter_estimator = DiscreteTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            DiscreteTreatmentOrthoForest.second_stage_parameter_estimator_gen(lambda_reg)
        # Define moment and mean gradient estimator
        moment_and_mean_gradient_estimator =\
            DiscreteTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        # Define autoencoder
        self._label_encoder = LabelEncoder()
        super(DiscreteTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,
            n_jobs=n_jobs,
            random_state=random_state)

def parameter_estimator_func(Y, T, X,
                                 nuisance_estimates,
                                 sample_weight=None):
        """Calculate the parameter of interest for points given by (Y, T) and corresponding nuisance estimates."""
        # Compute partial moments
        pointwise_params = DiscreteTreatmentOrthoForest._partial_moments(Y, T, nuisance_estimates)
        param_estimate = np.average(pointwise_params, weights=sample_weight, axis=0)
        # If any of the values in the parameter estimate is nan, return None
        return param_estimate

self.model_Y_final = clone(model_Y_final, safe=False)
        if self.propensity_model_final is None:
            self.propensity_model_final = clone(self.propensity_model, safe=False)
        if self.model_Y_final is None:
            self.model_Y_final = clone(self.model_Y, safe=False)
        # Nuisance estimators shall be defined during fitting because they need to know the number of distinct
        # treatments
        nuisance_estimator = None
        second_stage_nuisance_estimator = None
        # Define parameter estimators
        parameter_estimator = DiscreteTreatmentOrthoForest.parameter_estimator_func
        second_stage_parameter_estimator =\
            DiscreteTreatmentOrthoForest.second_stage_parameter_estimator_gen(lambda_reg)
        # Define moment and mean gradient estimator
        moment_and_mean_gradient_estimator =\
            DiscreteTreatmentOrthoForest.moment_and_mean_gradient_estimator_func
        # Define autoencoder
        self._label_encoder = LabelEncoder()
        super(DiscreteTreatmentOrthoForest, self).__init__(
            nuisance_estimator,
            second_stage_nuisance_estimator,
            parameter_estimator,
            second_stage_parameter_estimator,
            moment_and_mean_gradient_estimator,
            n_trees=n_trees,
            min_leaf_size=min_leaf_size,
            max_depth=max_depth,
            subsample_ratio=subsample_ratio,
            bootstrap=bootstrap,
            n_jobs=n_jobs,
            random_state=random_state)