and provide random values.
        """
        OperatorConfig = _import_operator_config()
        AnalyzerEngine = _import_analyzer_engine()
        NlpEngineProvider = _import_nlp_engine_provider()
        AnonymizerEngine = _import_anonymizer_engine()
        self.analyzed_fields = (
            analyzed_fields
            if analyzed_fields is not None
            else list(get_pseudoanonymizer_mapping().keys())
        )
        if add_default_faker_operators:
            self.operators = {
                field: OperatorConfig(
                    operator_name="custom", params={"lambda": faker_function}
                )
                for field, faker_function in get_pseudoanonymizer_mapping(
                    faker_seed
                ).items()
            }
        else:
            self.operators = {}
        if operators:
            self.add_operators(operators)
        provider = NlpEngineProvider(nlp_configuration=languages_config)
        nlp_engine = provider.create_engine()
        self.supported_languages = list(nlp_engine.nlp.keys())
        self._analyzer = AnalyzerEngine(
            supported_languages=self.supported_languages, nlp_engine=nlp_engine
        )
        self._anonymizer = AnonymizerEngine()
[docs]    def add_recognizer(self, recognizer: EntityRecognizer) -> None:
        """Add a recognizer to the analyzer
        Args:
            recognizer: Recognizer to add to the analyzer.
        """
        self._analyzer.registry.add_recognizer(recognizer)
        self.analyzed_fields.extend(recognizer.supported_entities)
[docs]    def add_operators(self, operators: Dict[str, OperatorConfig]) -> None:
        """Add operators to the anonymizer
        Args:
            operators: Operators to add to the anonymizer.
        """