Instruction:
Write a Python 3 function that can
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self,
                   'destination_field') and self.destination_field is not None:
            _dict['destination_field'] = self.destination_field
        if hasattr(self, 'source_field') and self.source_field is not None:
            _dict['source_field'] = self.source_field
        if hasattr(self, 'overwrite') and self.overwrite is not None:
            _dict['overwrite'] = self.overwrite
        if hasattr(self,
                   'enrichment_name') and self.enrichment_name is not None:
            _dict['enrichment'] = self.enrichment_name
        if hasattr(self, 'ignore_downstream_errors'
                  ) and self.ignore_downstream_errors is not None:
            _dict['ignore_downstream_errors'] = self.ignore_downstream_errors
        if hasattr(self, 'options') and self.options is not None:
            _dict['options'] = self.options._to_dict()
        return _dict


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'features') and self.features is not None:
            _dict['features'] = self.features._to_dict()
        if hasattr(self, 'language') and self.language is not None:
            _dict['language'] = self.language
        if hasattr(self, 'model') and self.model is not None:
            _dict['model'] = self.model
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate the documentation for the following Python 3 function
def _from_dict(cls, _dict):
        args = {}
        if 'environment_id' in _dict:
            args['environment_id'] = _dict.get('environment_id')
        if 'name' in _dict:
            args['name'] = _dict.get('name')
        if 'description' in _dict:
            args['description'] = _dict.get('description')
        if 'created' in _dict:
            args['created'] = string_to_datetime(_dict.get('created'))
        if 'updated' in _dict:
            args['updated'] = string_to_datetime(_dict.get('updated'))
        if 'status' in _dict:
            args['status'] = _dict.get('status')
        if 'read_only' in _dict:
            args['read_only'] = _dict.get('read_only')
        if 'size' in _dict:
            args['size'] = _dict.get('size')
        if 'requested_size' in _dict:
            args['requested_size'] = _dict.get('requested_size')
        if 'index_capacity' in _dict:
            args['index_capacity'] = IndexCapacity._from_dict(
                _dict.get('index_capacity'))
        if 'search_status' in _dict:
            args['search_status'] = SearchStatus._from_dict(
                _dict.get('search_status'))
        return cls(**args)

Response: Initialize a Environment object from a json dictionary.


Instruction:
Create a Python 3 function to
return a json dictionary representing this object.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'environment_id') and self.environment_id is not None:
            _dict['environment_id'] = self.environment_id
        if hasattr(self, 'name') and self.name is not None:
            _dict['name'] = self.name
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self, 'created') and self.created is not None:
            _dict['created'] = datetime_to_string(self.created)
        if hasattr(self, 'updated') and self.updated is not None:
            _dict['updated'] = datetime_to_string(self.updated)
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(self, 'read_only') and self.read_only is not None:
            _dict['read_only'] = self.read_only
        if hasattr(self, 'size') and self.size is not None:
            _dict['size'] = self.size
        if hasattr(self, 'requested_size') and self.requested_size is not None:
            _dict['requested_size'] = self.requested_size
        if hasattr(self, 'index_capacity') and self.index_capacity is not None:
            _dict['index_capacity'] = self.index_capacity._to_dict()
        if hasattr(self, 'search_status') and self.search_status is not None:
            _dict['search_status'] = self.search_status._to_dict()
        return _dict


Instruction:
Write a Python 3 script to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'indexed') and self.indexed is not None:
            _dict['indexed'] = self.indexed
        if hasattr(self,
                   'maximum_allowed') and self.maximum_allowed is not None:
            _dict['maximum_allowed'] = self.maximum_allowed
        return _dict


Instruction:
How would you code a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'environment_id') and self.environment_id is not None:
            _dict['environment_id'] = self.environment_id
        if hasattr(self, 'session_token') and self.session_token is not None:
            _dict['session_token'] = self.session_token
        if hasattr(self,
                   'client_timestamp') and self.client_timestamp is not None:
            _dict['client_timestamp'] = datetime_to_string(
                self.client_timestamp)
        if hasattr(self, 'display_rank') and self.display_rank is not None:
            _dict['display_rank'] = self.display_rank
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'query_id') and self.query_id is not None:
            _dict['query_id'] = self.query_id
        return _dict


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'input_terms') and self.input_terms is not None:
            _dict['input_terms'] = self.input_terms
        if hasattr(self, 'expanded_terms') and self.expanded_terms is not None:
            _dict['expanded_terms'] = self.expanded_terms
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Implement a Python 3 function for
initializing a Expansions object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a Expansions object from a json dictionary."""
        args = {}
        if 'expansions' in _dict:
            args['expansions'] = [
                Expansion._from_dict(x) for x in (_dict.get('expansions'))
            ]
        else:
            raise ValueError(
                'Required property \'expansions\' not present in Expansions JSON'
            )
        return cls(**args)


Instruction:
Can you create a Python 3 function that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'expansions') and self.expansions is not None:
            _dict['expansions'] = [x._to_dict() for x in self.expansions]
        return _dict


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'field_name') and self.field_name is not None:
            _dict['field'] = self.field_name
        if hasattr(self, 'field_type') and self.field_type is not None:
            _dict['type'] = self.field_type
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'match') and self.match is not None:
            _dict['match'] = self.match
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'level') and self.level is not None:
            _dict['level'] = self.level
        if hasattr(self, 'min_size') and self.min_size is not None:
            _dict['min_size'] = self.min_size
        if hasattr(self, 'max_size') and self.max_size is not None:
            _dict['max_size'] = self.max_size
        if hasattr(self, 'bold') and self.bold is not None:
            _dict['bold'] = self.bold
        if hasattr(self, 'italic') and self.italic is not None:
            _dict['italic'] = self.italic
        if hasattr(self, 'name') and self.name is not None:
            _dict['name'] = self.name
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Write a Python 3 script to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'gateway_id') and self.gateway_id is not None:
            _dict['gateway_id'] = self.gateway_id
        if hasattr(self, 'name') and self.name is not None:
            _dict['name'] = self.name
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(self, 'token') and self.token is not None:
            _dict['token'] = self.token
        if hasattr(self, 'token_id') and self.token_id is not None:
            _dict['token_id'] = self.token_id
        return _dict


Instruction:
Can you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'gateway_id') and self.gateway_id is not None:
            _dict['gateway_id'] = self.gateway_id
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        return _dict


Instruction:
Write a Python 3 script for
initializing a GatewayList object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a GatewayList object from a json dictionary."""
        args = {}
        if 'gateways' in _dict:
            args['gateways'] = [
                Gateway._from_dict(x) for x in (_dict.get('gateways'))
            ]
        return cls(**args)


Instruction:
Implement a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'gateways') and self.gateways is not None:
            _dict['gateways'] = [x._to_dict() for x in self.gateways]
        return _dict


Instruction:
Explain what the following Python 3 code does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'exclude_tags_completely'
                  ) and self.exclude_tags_completely is not None:
            _dict['exclude_tags_completely'] = self.exclude_tags_completely
        if hasattr(self, 'exclude_tags_keep_content'
                  ) and self.exclude_tags_keep_content is not None:
            _dict['exclude_tags_keep_content'] = self.exclude_tags_keep_content
        if hasattr(self, 'keep_content') and self.keep_content is not None:
            _dict['keep_content'] = self.keep_content._to_dict()
        if hasattr(self,
                   'exclude_content') and self.exclude_content is not None:
            _dict['exclude_content'] = self.exclude_content._to_dict()
        if hasattr(
                self,
                'keep_tag_attributes') and self.keep_tag_attributes is not None:
            _dict['keep_tag_attributes'] = self.keep_tag_attributes
        if hasattr(self, 'exclude_tag_attributes'
                  ) and self.exclude_tag_attributes is not None:
            _dict['exclude_tag_attributes'] = self.exclude_tag_attributes
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you create a Python 3 function that
initializes a IndexCapacity object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a IndexCapacity object from a json dictionary."""
        args = {}
        if 'documents' in _dict:
            args['documents'] = EnvironmentDocuments._from_dict(
                _dict.get('documents'))
        if 'disk_usage' in _dict:
            args['disk_usage'] = DiskUsage._from_dict(_dict.get('disk_usage'))
        if 'collections' in _dict:
            args['collections'] = CollectionUsage._from_dict(
                _dict.get('collections'))
        return cls(**args)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'documents') and self.documents is not None:
            _dict['documents'] = self.documents._to_dict()
        if hasattr(self, 'disk_usage') and self.disk_usage is not None:
            _dict['disk_usage'] = self.disk_usage._to_dict()
        if hasattr(self, 'collections') and self.collections is not None:
            _dict['collections'] = self.collections._to_dict()
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Write a Python 3 function that can
initialize a ListCollectionFieldsResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a ListCollectionFieldsResponse object from a json dictionary."""
        args = {}
        if 'fields' in _dict:
            args['fields'] = [
                Field._from_dict(x) for x in (_dict.get('fields'))
            ]
        return cls(**args)


Instruction:
Make a summary of the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'fields') and self.fields is not None:
            _dict['fields'] = [x._to_dict() for x in self.fields]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'collections' in _dict:
            args['collections'] = [
                Collection._from_dict(x) for x in (_dict.get('collections'))
            ]
        return cls(**args)

Response: Initialize a ListCollectionsResponse object from a json dictionary.


Instruction:
Can you write a function in Python 3 where it
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'collections') and self.collections is not None:
            _dict['collections'] = [x._to_dict() for x in self.collections]
        return _dict


Instruction:
Write a Python 3 function for
initializing a ListConfigurationsResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a ListConfigurationsResponse object from a json dictionary."""
        args = {}
        if 'configurations' in _dict:
            args['configurations'] = [
                Configuration._from_dict(x)
                for x in (_dict.get('configurations'))
            ]
        return cls(**args)


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'configurations') and self.configurations is not None:
            _dict['configurations'] = [
                x._to_dict() for x in self.configurations
            ]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'environments' in _dict:
            args['environments'] = [
                Environment._from_dict(x) for x in (_dict.get('environments'))
            ]
        return cls(**args)

Response: Initialize a ListEnvironmentsResponse object from a json dictionary.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'environments') and self.environments is not None:
            _dict['environments'] = [x._to_dict() for x in self.environments]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you create a Python 3 function that
initializes a LogQueryResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a LogQueryResponse object from a json dictionary."""
        args = {}
        if 'matching_results' in _dict:
            args['matching_results'] = _dict.get('matching_results')
        if 'results' in _dict:
            args['results'] = [
                LogQueryResponseResult._from_dict(x)
                for x in (_dict.get('results'))
            ]
        return cls(**args)


Instruction:
Create a Python 3 function to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'results') and self.results is not None:
            _dict['results'] = [x._to_dict() for x in self.results]
        return _dict


Instruction:
Can you tell what is the following Python 3 function doing
def _from_dict(cls, _dict):
        args = {}
        if 'environment_id' in _dict:
            args['environment_id'] = _dict.get('environment_id')
        if 'customer_id' in _dict:
            args['customer_id'] = _dict.get('customer_id')
        if 'document_type' in _dict:
            args['document_type'] = _dict.get('document_type')
        if 'natural_language_query' in _dict:
            args['natural_language_query'] = _dict.get('natural_language_query')
        if 'document_results' in _dict:
            args[
                'document_results'] = LogQueryResponseResultDocuments._from_dict(
                    _dict.get('document_results'))
        if 'created_timestamp' in _dict:
            args['created_timestamp'] = string_to_datetime(
                _dict.get('created_timestamp'))
        if 'client_timestamp' in _dict:
            args['client_timestamp'] = string_to_datetime(
                _dict.get('client_timestamp'))
        if 'query_id' in _dict:
            args['query_id'] = _dict.get('query_id')
        if 'session_token' in _dict:
            args['session_token'] = _dict.get('session_token')
        if 'collection_id' in _dict:
            args['collection_id'] = _dict.get('collection_id')
        if 'display_rank' in _dict:
            args['display_rank'] = _dict.get('display_rank')
        if 'document_id' in _dict:
            args['document_id'] = _dict.get('document_id')
        if 'event_type' in _dict:
            args['event_type'] = _dict.get('event_type')
        if 'result_type' in _dict:
            args['result_type'] = _dict.get('result_type')
        return cls(**args)

Response: Initialize a LogQueryResponseResult object from a json dictionary.


Instruction:
Create a Python 3 function to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'environment_id') and self.environment_id is not None:
            _dict['environment_id'] = self.environment_id
        if hasattr(self, 'customer_id') and self.customer_id is not None:
            _dict['customer_id'] = self.customer_id
        if hasattr(self, 'document_type') and self.document_type is not None:
            _dict['document_type'] = self.document_type
        if hasattr(self, 'natural_language_query'
                  ) and self.natural_language_query is not None:
            _dict['natural_language_query'] = self.natural_language_query
        if hasattr(self,
                   'document_results') and self.document_results is not None:
            _dict['document_results'] = self.document_results._to_dict()
        if hasattr(self,
                   'created_timestamp') and self.created_timestamp is not None:
            _dict['created_timestamp'] = datetime_to_string(
                self.created_timestamp)
        if hasattr(self,
                   'client_timestamp') and self.client_timestamp is not None:
            _dict['client_timestamp'] = datetime_to_string(
                self.client_timestamp)
        if hasattr(self, 'query_id') and self.query_id is not None:
            _dict['query_id'] = self.query_id
        if hasattr(self, 'session_token') and self.session_token is not None:
            _dict['session_token'] = self.session_token
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        if hasattr(self, 'display_rank') and self.display_rank is not None:
            _dict['display_rank'] = self.display_rank
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'event_type') and self.event_type is not None:
            _dict['event_type'] = self.event_type
        if hasattr(self, 'result_type') and self.result_type is not None:
            _dict['result_type'] = self.result_type
        return _dict


Instruction:
Can you generate the documentation for the following Python 3 function
def _from_dict(cls, _dict):
        args = {}
        if 'results' in _dict:
            args['results'] = [
                LogQueryResponseResultDocumentsResult._from_dict(x)
                for x in (_dict.get('results'))
            ]
        if 'count' in _dict:
            args['count'] = _dict.get('count')
        return cls(**args)

Response: Initialize a LogQueryResponseResultDocuments object from a json dictionary.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'results') and self.results is not None:
            _dict['results'] = [x._to_dict() for x in self.results]
        if hasattr(self, 'count') and self.count is not None:
            _dict['count'] = self.count
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'position') and self.position is not None:
            _dict['position'] = self.position
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'score') and self.score is not None:
            _dict['score'] = self.score
        if hasattr(self, 'confidence') and self.confidence is not None:
            _dict['confidence'] = self.confidence
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'interval' in _dict:
            args['interval'] = _dict.get('interval')
        if 'event_type' in _dict:
            args['event_type'] = _dict.get('event_type')
        if 'results' in _dict:
            args['results'] = [
                MetricAggregationResult._from_dict(x)
                for x in (_dict.get('results'))
            ]
        return cls(**args)

Response: Initialize a MetricAggregation object from a json dictionary.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'interval') and self.interval is not None:
            _dict['interval'] = self.interval
        if hasattr(self, 'event_type') and self.event_type is not None:
            _dict['event_type'] = self.event_type
        if hasattr(self, 'results') and self.results is not None:
            _dict['results'] = [x._to_dict() for x in self.results]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Create a Python 3 function to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'key_as_string') and self.key_as_string is not None:
            _dict['key_as_string'] = datetime_to_string(self.key_as_string)
        if hasattr(self, 'key') and self.key is not None:
            _dict['key'] = self.key
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'event_rate') and self.event_rate is not None:
            _dict['event_rate'] = self.event_rate
        return _dict


Instruction:
Create a Python 3 function to
initialize a MetricResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a MetricResponse object from a json dictionary."""
        args = {}
        if 'aggregations' in _dict:
            args['aggregations'] = [
                MetricAggregation._from_dict(x)
                for x in (_dict.get('aggregations'))
            ]
        return cls(**args)


Instruction:
Make a summary of the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'aggregations') and self.aggregations is not None:
            _dict['aggregations'] = [x._to_dict() for x in self.aggregations]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you implement a function in Python 3 that
initializes a MetricTokenAggregation object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a MetricTokenAggregation object from a json dictionary."""
        args = {}
        if 'event_type' in _dict:
            args['event_type'] = _dict.get('event_type')
        if 'results' in _dict:
            args['results'] = [
                MetricTokenAggregationResult._from_dict(x)
                for x in (_dict.get('results'))
            ]
        return cls(**args)


Instruction:
Can you create a Python 3 function that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'key') and self.key is not None:
            _dict['key'] = self.key
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'event_rate') and self.event_rate is not None:
            _dict['event_rate'] = self.event_rate
        return _dict


Instruction:
Can you write a function in Python 3 where it
initializes a MetricTokenResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a MetricTokenResponse object from a json dictionary."""
        args = {}
        if 'aggregations' in _dict:
            args['aggregations'] = [
                MetricTokenAggregation._from_dict(x)
                for x in (_dict.get('aggregations'))
            ]
        return cls(**args)


Instruction:
Write a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'path') and self.path is not None:
            _dict['path'] = self.path
        return _dict


Instruction:
Implement a function in Python 3 to
initialize a NluEnrichmentFeatures object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a NluEnrichmentFeatures object from a json dictionary."""
        args = {}
        if 'keywords' in _dict:
            args['keywords'] = NluEnrichmentKeywords._from_dict(
                _dict.get('keywords'))
        if 'entities' in _dict:
            args['entities'] = NluEnrichmentEntities._from_dict(
                _dict.get('entities'))
        if 'sentiment' in _dict:
            args['sentiment'] = NluEnrichmentSentiment._from_dict(
                _dict.get('sentiment'))
        if 'emotion' in _dict:
            args['emotion'] = NluEnrichmentEmotion._from_dict(
                _dict.get('emotion'))
        if 'categories' in _dict:
            args['categories'] = NluEnrichmentCategories._from_dict(
                _dict.get('categories'))
        if 'semantic_roles' in _dict:
            args['semantic_roles'] = NluEnrichmentSemanticRoles._from_dict(
                _dict.get('semantic_roles'))
        if 'relations' in _dict:
            args['relations'] = NluEnrichmentRelations._from_dict(
                _dict.get('relations'))
        if 'concepts' in _dict:
            args['concepts'] = NluEnrichmentConcepts._from_dict(
                _dict.get('concepts'))
        return cls(**args)


Instruction:
Explain what the following Python 3 code does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'sentiment') and self.sentiment is not None:
            _dict['sentiment'] = self.sentiment
        if hasattr(self, 'emotion') and self.emotion is not None:
            _dict['emotion'] = self.emotion
        if hasattr(self, 'limit') and self.limit is not None:
            _dict['limit'] = self.limit
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you write a function in Python 3 where it
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'operation') and self.operation is not None:
            _dict['operation'] = self.operation
        if hasattr(self, 'source_field') and self.source_field is not None:
            _dict['source_field'] = self.source_field
        if hasattr(self,
                   'destination_field') and self.destination_field is not None:
            _dict['destination_field'] = self.destination_field
        return _dict


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'notice_id') and self.notice_id is not None:
            _dict['notice_id'] = self.notice_id
        if hasattr(self, 'created') and self.created is not None:
            _dict['created'] = datetime_to_string(self.created)
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'query_id') and self.query_id is not None:
            _dict['query_id'] = self.query_id
        if hasattr(self, 'severity') and self.severity is not None:
            _dict['severity'] = self.severity
        if hasattr(self, 'step') and self.step is not None:
            _dict['step'] = self.step
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Implement a Python 3 function for
initializing a PdfHeadingDetection object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a PdfHeadingDetection object from a json dictionary."""
        args = {}
        if 'fonts' in _dict:
            args['fonts'] = [
                FontSetting._from_dict(x) for x in (_dict.get('fonts'))
            ]
        return cls(**args)


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'fonts') and self.fonts is not None:
            _dict['fonts'] = [x._to_dict() for x in self.fonts]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Create a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'heading') and self.heading is not None:
            _dict['heading'] = self.heading._to_dict()
        return _dict


Instruction:
Can you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        if hasattr(self, 'results') and self.results is not None:
            _dict['results'] = [x._to_dict() for x in self.results]
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'aggregations') and self.aggregations is not None:
            _dict['aggregations'] = [x._to_dict() for x in self.aggregations]
        return _dict


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'entities' in _dict:
            args['entities'] = [
                QueryEntitiesResponseItem._from_dict(x)
                for x in (_dict.get('entities'))
            ]
        return cls(**args)

Response: Initialize a QueryEntitiesResponse object from a json dictionary.


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'entities') and self.entities is not None:
            _dict['entities'] = [x._to_dict() for x in self.entities]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
How would you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        if hasattr(self, 'evidence') and self.evidence is not None:
            _dict['evidence'] = [x._to_dict() for x in self.evidence]
        return _dict


Instruction:
Can you write a function in Python 3 where it
initializes a QueryEvidence object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a QueryEvidence object from a json dictionary."""
        args = {}
        if 'document_id' in _dict:
            args['document_id'] = _dict.get('document_id')
        if 'field' in _dict:
            args['field'] = _dict.get('field')
        if 'start_offset' in _dict:
            args['start_offset'] = _dict.get('start_offset')
        if 'end_offset' in _dict:
            args['end_offset'] = _dict.get('end_offset')
        if 'entities' in _dict:
            args['entities'] = [
                QueryEvidenceEntity._from_dict(x)
                for x in (_dict.get('entities'))
            ]
        return cls(**args)


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'field') and self.field is not None:
            _dict['field'] = self.field
        if hasattr(self, 'start_offset') and self.start_offset is not None:
            _dict['start_offset'] = self.start_offset
        if hasattr(self, 'end_offset') and self.end_offset is not None:
            _dict['end_offset'] = self.end_offset
        if hasattr(self, 'entities') and self.entities is not None:
            _dict['entities'] = [x._to_dict() for x in self.entities]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'start_offset') and self.start_offset is not None:
            _dict['start_offset'] = self.start_offset
        if hasattr(self, 'end_offset') and self.end_offset is not None:
            _dict['end_offset'] = self.end_offset
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Write a Python 3 script to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'exclude') and self.exclude is not None:
            _dict['exclude'] = self.exclude
        if hasattr(self, 'include') and self.include is not None:
            _dict['include'] = self.include
        return _dict


Instruction:
Can you tell what is the following Python 3 function doing
def _from_dict(cls, _dict):
        args = {}
        if 'matching_results' in _dict:
            args['matching_results'] = _dict.get('matching_results')
        if 'results' in _dict:
            args['results'] = [
                QueryNoticesResult._from_dict(x) for x in (_dict.get('results'))
            ]
        if 'aggregations' in _dict:
            args['aggregations'] = [
                QueryAggregation._from_dict(x)
                for x in (_dict.get('aggregations'))
            ]
        if 'passages' in _dict:
            args['passages'] = [
                QueryPassages._from_dict(x) for x in (_dict.get('passages'))
            ]
        if 'duplicates_removed' in _dict:
            args['duplicates_removed'] = _dict.get('duplicates_removed')
        return cls(**args)

Response: Initialize a QueryNoticesResponse object from a json dictionary.


Instruction:
Implement a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'results') and self.results is not None:
            _dict['results'] = [x._to_dict() for x in self.results]
        if hasattr(self, 'aggregations') and self.aggregations is not None:
            _dict['aggregations'] = [x._to_dict() for x in self.aggregations]
        if hasattr(self, 'passages') and self.passages is not None:
            _dict['passages'] = [x._to_dict() for x in self.passages]
        if hasattr(
                self,
                'duplicates_removed') and self.duplicates_removed is not None:
            _dict['duplicates_removed'] = self.duplicates_removed
        return _dict


Instruction:
Here you have a function in Python 3, explain what it does
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'id' in _dict:
            args['id'] = _dict.get('id')
            del xtra['id']
        if 'metadata' in _dict:
            args['metadata'] = _dict.get('metadata')
            del xtra['metadata']
        if 'collection_id' in _dict:
            args['collection_id'] = _dict.get('collection_id')
            del xtra['collection_id']
        if 'result_metadata' in _dict:
            args['result_metadata'] = QueryResultMetadata._from_dict(
                _dict.get('result_metadata'))
            del xtra['result_metadata']
        if 'title' in _dict:
            args['title'] = _dict.get('title')
            del xtra['title']
        if 'code' in _dict:
            args['code'] = _dict.get('code')
            del xtra['code']
        if 'filename' in _dict:
            args['filename'] = _dict.get('filename')
            del xtra['filename']
        if 'file_type' in _dict:
            args['file_type'] = _dict.get('file_type')
            del xtra['file_type']
        if 'sha1' in _dict:
            args['sha1'] = _dict.get('sha1')
            del xtra['sha1']
        if 'notices' in _dict:
            args['notices'] = [
                Notice._from_dict(x) for x in (_dict.get('notices'))
            ]
            del xtra['notices']
        args.update(xtra)
        return cls(**args)

Response: Initialize a QueryNoticesResult object from a json dictionary.


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'id') and self.id is not None:
            _dict['id'] = self.id
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        if hasattr(self,
                   'result_metadata') and self.result_metadata is not None:
            _dict['result_metadata'] = self.result_metadata._to_dict()
        if hasattr(self, 'title') and self.title is not None:
            _dict['title'] = self.title
        if hasattr(self, 'code') and self.code is not None:
            _dict['code'] = self.code
        if hasattr(self, 'filename') and self.filename is not None:
            _dict['filename'] = self.filename
        if hasattr(self, 'file_type') and self.file_type is not None:
            _dict['file_type'] = self.file_type
        if hasattr(self, 'sha1') and self.sha1 is not None:
            _dict['sha1'] = self.sha1
        if hasattr(self, 'notices') and self.notices is not None:
            _dict['notices'] = [x._to_dict() for x in self.notices]
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self, 'passage_score') and self.passage_score is not None:
            _dict['passage_score'] = self.passage_score
        if hasattr(self, 'passage_text') and self.passage_text is not None:
            _dict['passage_text'] = self.passage_text
        if hasattr(self, 'start_offset') and self.start_offset is not None:
            _dict['start_offset'] = self.start_offset
        if hasattr(self, 'end_offset') and self.end_offset is not None:
            _dict['end_offset'] = self.end_offset
        if hasattr(self, 'field') and self.field is not None:
            _dict['field'] = self.field
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Implement a function in Python 3 to
initialize a QueryRelationsArgument object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a QueryRelationsArgument object from a json dictionary."""
        args = {}
        if 'entities' in _dict:
            args['entities'] = [
                QueryEntitiesEntity._from_dict(x)
                for x in (_dict.get('entities'))
            ]
        return cls(**args)


Instruction:
Make a summary of the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'relation_types') and self.relation_types is not None:
            _dict['relation_types'] = self.relation_types._to_dict()
        if hasattr(self, 'entity_types') and self.entity_types is not None:
            _dict['entity_types'] = self.entity_types._to_dict()
        if hasattr(self, 'document_ids') and self.document_ids is not None:
            _dict['document_ids'] = self.document_ids
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'type' in _dict:
            args['type'] = _dict.get('type')
        if 'frequency' in _dict:
            args['frequency'] = _dict.get('frequency')
        if 'arguments' in _dict:
            args['arguments'] = [
                QueryRelationsArgument._from_dict(x)
                for x in (_dict.get('arguments'))
            ]
        if 'evidence' in _dict:
            args['evidence'] = [
                QueryEvidence._from_dict(x) for x in (_dict.get('evidence'))
            ]
        return cls(**args)

Response: Initialize a QueryRelationsRelationship object from a json dictionary.


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        if hasattr(self, 'frequency') and self.frequency is not None:
            _dict['frequency'] = self.frequency
        if hasattr(self, 'arguments') and self.arguments is not None:
            _dict['arguments'] = [x._to_dict() for x in self.arguments]
        if hasattr(self, 'evidence') and self.evidence is not None:
            _dict['evidence'] = [x._to_dict() for x in self.evidence]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'relations' in _dict:
            args['relations'] = [
                QueryRelationsRelationship._from_dict(x)
                for x in (_dict.get('relations'))
            ]
        return cls(**args)

Response: Initialize a QueryRelationsResponse object from a json dictionary.


Instruction:
How would you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'relations') and self.relations is not None:
            _dict['relations'] = [x._to_dict() for x in self.relations]
        return _dict


Instruction:
Write a Python 3 function for
initializing a QueryResponse object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a QueryResponse object from a json dictionary."""
        args = {}
        if 'matching_results' in _dict:
            args['matching_results'] = _dict.get('matching_results')
        if 'results' in _dict:
            args['results'] = [
                QueryResult._from_dict(x) for x in (_dict.get('results'))
            ]
        if 'aggregations' in _dict:
            args['aggregations'] = [
                QueryAggregation._from_dict(x)
                for x in (_dict.get('aggregations'))
            ]
        if 'passages' in _dict:
            args['passages'] = [
                QueryPassages._from_dict(x) for x in (_dict.get('passages'))
            ]
        if 'duplicates_removed' in _dict:
            args['duplicates_removed'] = _dict.get('duplicates_removed')
        if 'session_token' in _dict:
            args['session_token'] = _dict.get('session_token')
        if 'retrieval_details' in _dict:
            args['retrieval_details'] = RetrievalDetails._from_dict(
                _dict.get('retrieval_details'))
        return cls(**args)


Instruction:
Given the following Python 3 function, write the documentation
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'id' in _dict:
            args['id'] = _dict.get('id')
            del xtra['id']
        if 'metadata' in _dict:
            args['metadata'] = _dict.get('metadata')
            del xtra['metadata']
        if 'collection_id' in _dict:
            args['collection_id'] = _dict.get('collection_id')
            del xtra['collection_id']
        if 'result_metadata' in _dict:
            args['result_metadata'] = QueryResultMetadata._from_dict(
                _dict.get('result_metadata'))
            del xtra['result_metadata']
        if 'title' in _dict:
            args['title'] = _dict.get('title')
            del xtra['title']
        args.update(xtra)
        return cls(**args)

Response: Initialize a QueryResult object from a json dictionary.


Instruction:
Here you have a function in Python 3, explain what it does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'id') and self.id is not None:
            _dict['id'] = self.id
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        if hasattr(self,
                   'result_metadata') and self.result_metadata is not None:
            _dict['result_metadata'] = self.result_metadata._to_dict()
        if hasattr(self, 'title') and self.title is not None:
            _dict['title'] = self.title
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'score') and self.score is not None:
            _dict['score'] = self.score
        if hasattr(self, 'confidence') and self.confidence is not None:
            _dict['confidence'] = self.confidence
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Write a Python 3 script for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'document_retrieval_strategy'
                  ) and self.document_retrieval_strategy is not None:
            _dict[
                'document_retrieval_strategy'] = self.document_retrieval_strategy
        return _dict


Instruction:
How would you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'scope') and self.scope is not None:
            _dict['scope'] = self.scope
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(
                self,
                'status_description') and self.status_description is not None:
            _dict['status_description'] = self.status_description
        if hasattr(self, 'last_trained') and self.last_trained is not None:
            _dict['last_trained'] = self.last_trained
        return _dict


Instruction:
Implement a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'enabled') and self.enabled is not None:
            _dict['enabled'] = self.enabled
        if hasattr(self, 'selector_tags') and self.selector_tags is not None:
            _dict['selector_tags'] = self.selector_tags
        return _dict


Instruction:
Explain what the following Python 3 code does
def _from_dict(cls, _dict):
        args = {}
        if 'type' in _dict:
            args['type'] = _dict.get('type')
        if 'credential_id' in _dict:
            args['credential_id'] = _dict.get('credential_id')
        if 'schedule' in _dict:
            args['schedule'] = SourceSchedule._from_dict(_dict.get('schedule'))
        if 'options' in _dict:
            args['options'] = SourceOptions._from_dict(_dict.get('options'))
        return cls(**args)

Response: Initialize a Source object from a json dictionary.


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        if hasattr(self, 'credential_id') and self.credential_id is not None:
            _dict['credential_id'] = self.credential_id
        if hasattr(self, 'schedule') and self.schedule is not None:
            _dict['schedule'] = self.schedule._to_dict()
        if hasattr(self, 'options') and self.options is not None:
            _dict['options'] = self.options._to_dict()
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'folders' in _dict:
            args['folders'] = [
                SourceOptionsFolder._from_dict(x)
                for x in (_dict.get('folders'))
            ]
        if 'objects' in _dict:
            args['objects'] = [
                SourceOptionsObject._from_dict(x)
                for x in (_dict.get('objects'))
            ]
        if 'site_collections' in _dict:
            args['site_collections'] = [
                SourceOptionsSiteColl._from_dict(x)
                for x in (_dict.get('site_collections'))
            ]
        if 'urls' in _dict:
            args['urls'] = [
                SourceOptionsWebCrawl._from_dict(x) for x in (_dict.get('urls'))
            ]
        if 'buckets' in _dict:
            args['buckets'] = [
                SourceOptionsBuckets._from_dict(x)
                for x in (_dict.get('buckets'))
            ]
        if 'crawl_all_buckets' in _dict:
            args['crawl_all_buckets'] = _dict.get('crawl_all_buckets')
        return cls(**args)

Response: Initialize a SourceOptions object from a json dictionary.


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'folders') and self.folders is not None:
            _dict['folders'] = [x._to_dict() for x in self.folders]
        if hasattr(self, 'objects') and self.objects is not None:
            _dict['objects'] = [x._to_dict() for x in self.objects]
        if hasattr(self,
                   'site_collections') and self.site_collections is not None:
            _dict['site_collections'] = [
                x._to_dict() for x in self.site_collections
            ]
        if hasattr(self, 'urls') and self.urls is not None:
            _dict['urls'] = [x._to_dict() for x in self.urls]
        if hasattr(self, 'buckets') and self.buckets is not None:
            _dict['buckets'] = [x._to_dict() for x in self.buckets]
        if hasattr(self,
                   'crawl_all_buckets') and self.crawl_all_buckets is not None:
            _dict['crawl_all_buckets'] = self.crawl_all_buckets
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'name') and self.name is not None:
            _dict['name'] = self.name
        if hasattr(self, 'limit') and self.limit is not None:
            _dict['limit'] = self.limit
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'owner_user_id') and self.owner_user_id is not None:
            _dict['owner_user_id'] = self.owner_user_id
        if hasattr(self, 'folder_id') and self.folder_id is not None:
            _dict['folder_id'] = self.folder_id
        if hasattr(self, 'limit') and self.limit is not None:
            _dict['limit'] = self.limit
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Implement a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'site_collection_path'
                  ) and self.site_collection_path is not None:
            _dict['site_collection_path'] = self.site_collection_path
        if hasattr(self, 'limit') and self.limit is not None:
            _dict['limit'] = self.limit
        return _dict


Instruction:
Here you have a function in Python 3, explain what it does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'url') and self.url is not None:
            _dict['url'] = self.url
        if hasattr(self, 'limit_to_starting_hosts'
                  ) and self.limit_to_starting_hosts is not None:
            _dict['limit_to_starting_hosts'] = self.limit_to_starting_hosts
        if hasattr(self, 'crawl_speed') and self.crawl_speed is not None:
            _dict['crawl_speed'] = self.crawl_speed
        if hasattr(self, 'allow_untrusted_certificate'
                  ) and self.allow_untrusted_certificate is not None:
            _dict[
                'allow_untrusted_certificate'] = self.allow_untrusted_certificate
        if hasattr(self, 'maximum_hops') and self.maximum_hops is not None:
            _dict['maximum_hops'] = self.maximum_hops
        if hasattr(self,
                   'request_timeout') and self.request_timeout is not None:
            _dict['request_timeout'] = self.request_timeout
        if hasattr(
                self,
                'override_robots_txt') and self.override_robots_txt is not None:
            _dict['override_robots_txt'] = self.override_robots_txt
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'enabled') and self.enabled is not None:
            _dict['enabled'] = self.enabled
        if hasattr(self, 'time_zone') and self.time_zone is not None:
            _dict['time_zone'] = self.time_zone
        if hasattr(self, 'frequency') and self.frequency is not None:
            _dict['frequency'] = self.frequency
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(self, 'last_updated') and self.last_updated is not None:
            _dict['last_updated'] = datetime_to_string(self.last_updated)
        return _dict


Instruction:
Create a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'field') and self.field is not None:
            _dict['field'] = self.field
        if hasattr(self, 'count') and self.count is not None:
            _dict['count'] = self.count
        return _dict


Instruction:
Write a Python 3 script for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'field') and self.field is not None:
            _dict['field'] = self.field
        if hasattr(self, 'interval') and self.interval is not None:
            _dict['interval'] = self.interval
        if hasattr(self, 'anomaly') and self.anomaly is not None:
            _dict['anomaly'] = self.anomaly
        return _dict


Instruction:
Write a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'tokens') and self.tokens is not None:
            _dict['tokens'] = self.tokens
        if hasattr(self, 'readings') and self.readings is not None:
            _dict['readings'] = self.readings
        if hasattr(self, 'part_of_speech') and self.part_of_speech is not None:
            _dict['part_of_speech'] = self.part_of_speech
        return _dict


Instruction:
How would you code a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(self, 'type') and self.type is not None:
            _dict['type'] = self.type
        return _dict


Instruction:
Write a Python 3 script for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'size') and self.size is not None:
            _dict['size'] = self.size
        if hasattr(self, 'hits') and self.hits is not None:
            _dict['hits'] = self.hits._to_dict()
        return _dict


Instruction:
Can you write a function in Python 3 where it
initializes a TopHitsResults object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a TopHitsResults object from a json dictionary."""
        args = {}
        if 'matching_results' in _dict:
            args['matching_results'] = _dict.get('matching_results')
        if 'hits' in _dict:
            args['hits'] = [
                QueryResult._from_dict(x) for x in (_dict.get('hits'))
            ]
        return cls(**args)


Instruction:
Write a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self,
                   'matching_results') and self.matching_results is not None:
            _dict['matching_results'] = self.matching_results
        if hasattr(self, 'hits') and self.hits is not None:
            _dict['hits'] = [x._to_dict() for x in self.hits]
        return _dict


Instruction:
Explain what the following Python 3 code does
def _from_dict(cls, _dict):
        args = {}
        if 'environment_id' in _dict:
            args['environment_id'] = _dict.get('environment_id')
        if 'collection_id' in _dict:
            args['collection_id'] = _dict.get('collection_id')
        if 'queries' in _dict:
            args['queries'] = [
                TrainingQuery._from_dict(x) for x in (_dict.get('queries'))
            ]
        return cls(**args)

Response: Initialize a TrainingDataSet object from a json dictionary.


Instruction:
Explain what the following Python 3 code does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'environment_id') and self.environment_id is not None:
            _dict['environment_id'] = self.environment_id
        if hasattr(self, 'collection_id') and self.collection_id is not None:
            _dict['collection_id'] = self.collection_id
        if hasattr(self, 'queries') and self.queries is not None:
            _dict['queries'] = [x._to_dict() for x in self.queries]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Create a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'document_id') and self.document_id is not None:
            _dict['document_id'] = self.document_id
        if hasattr(self,
                   'cross_reference') and self.cross_reference is not None:
            _dict['cross_reference'] = self.cross_reference
        if hasattr(self, 'relevance') and self.relevance is not None:
            _dict['relevance'] = self.relevance
        return _dict


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'examples' in _dict:
            args['examples'] = [
                TrainingExample._from_dict(x) for x in (_dict.get('examples'))
            ]
        return cls(**args)

Response: Initialize a TrainingExampleList object from a json dictionary.


Instruction:
How would you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'examples') and self.examples is not None:
            _dict['examples'] = [x._to_dict() for x in self.examples]
        return _dict


Instruction:
How would you explain what the following Python 3 function does
def _from_dict(cls, _dict):
        args = {}
        if 'query_id' in _dict:
            args['query_id'] = _dict.get('query_id')
        if 'natural_language_query' in _dict:
            args['natural_language_query'] = _dict.get('natural_language_query')
        if 'filter' in _dict:
            args['filter'] = _dict.get('filter')
        if 'examples' in _dict:
            args['examples'] = [
                TrainingExample._from_dict(x) for x in (_dict.get('examples'))
            ]
        return cls(**args)

Response: Initialize a TrainingQuery object from a json dictionary.


Instruction:
How would you code a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'query_id') and self.query_id is not None:
            _dict['query_id'] = self.query_id
        if hasattr(self, 'natural_language_query'
                  ) and self.natural_language_query is not None:
            _dict['natural_language_query'] = self.natural_language_query
        if hasattr(self, 'filter') and self.filter is not None:
            _dict['filter'] = self.filter
        if hasattr(self, 'examples') and self.examples is not None:
            _dict['examples'] = [x._to_dict() for x in self.examples]
        return _dict


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'total_examples') and self.total_examples is not None:
            _dict['total_examples'] = self.total_examples
        if hasattr(self, 'available') and self.available is not None:
            _dict['available'] = self.available
        if hasattr(self, 'processing') and self.processing is not None:
            _dict['processing'] = self.processing
        if hasattr(self, 'minimum_queries_added'
                  ) and self.minimum_queries_added is not None:
            _dict['minimum_queries_added'] = self.minimum_queries_added
        if hasattr(self, 'minimum_examples_added'
                  ) and self.minimum_examples_added is not None:
            _dict['minimum_examples_added'] = self.minimum_examples_added
        if hasattr(self, 'sufficient_label_diversity'
                  ) and self.sufficient_label_diversity is not None:
            _dict[
                'sufficient_label_diversity'] = self.sufficient_label_diversity
        if hasattr(self, 'notices') and self.notices is not None:
            _dict['notices'] = self.notices
        if hasattr(self, 'successfully_trained'
                  ) and self.successfully_trained is not None:
            _dict['successfully_trained'] = datetime_to_string(
                self.successfully_trained)
        if hasattr(self, 'data_updated') and self.data_updated is not None:
            _dict['data_updated'] = datetime_to_string(self.data_updated)
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you implement a function in Python 3 that
initializes a WordHeadingDetection object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a WordHeadingDetection object from a json dictionary."""
        args = {}
        if 'fonts' in _dict:
            args['fonts'] = [
                FontSetting._from_dict(x) for x in (_dict.get('fonts'))
            ]
        if 'styles' in _dict:
            args['styles'] = [
                WordStyle._from_dict(x) for x in (_dict.get('styles'))
            ]
        return cls(**args)


Instruction:
Write a Python 3 script to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'fonts') and self.fonts is not None:
            _dict['fonts'] = [x._to_dict() for x in self.fonts]
        if hasattr(self, 'styles') and self.styles is not None:
            _dict['styles'] = [x._to_dict() for x in self.styles]
        return _dict


Instruction:
Here you have a function in Python 3, explain what it does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'level') and self.level is not None:
            _dict['level'] = self.level
        if hasattr(self, 'names') and self.names is not None:
            _dict['names'] = self.names
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you create a Python 3 function that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'xpaths') and self.xpaths is not None:
            _dict['xpaths'] = self.xpaths
        return _dict


Instruction:
Can you create a Python 3 function that
analyzes a tone of a full document and return a dictionary of the result.

Response:
def tone(self,
             tone_input,
             sentences=None,
             tones=None,
             content_language=None,
             accept_language=None,
             content_type=None,
             **kwargs):
        """
        Analyze general tone.

        Use the general purpose endpoint to analyze the tone of your input content. The
        service analyzes the content for emotional and language tones. The method always
        analyzes the tone of the full document; by default, it also analyzes the tone of
        each individual sentence of the content.
        You can submit no more than 128 KB of total input content and no more than 1000
        individual sentences in JSON, plain text, or HTML format. The service analyzes the
        first 1000 sentences for document-level analysis and only the first 100 sentences
        for sentence-level analysis.
        Per the JSON specification, the default character encoding for JSON content is
        effectively always UTF-8; per the HTTP specification, the default encoding for
        plain text and HTML is ISO-8859-1 (effectively, the ASCII character set). When
        specifying a content type of plain text or HTML, include the `charset` parameter
        to indicate the character encoding of the input text; for example: `Content-Type:
        text/plain;charset=utf-8`. For `text/html`, the service removes HTML tags and
        analyzes only the textual content.
        **See also:** [Using the general-purpose
        endpoint](https://cloud.ibm.com/docs/services/tone-analyzer/using-tone.html#using-the-general-purpose-endpoint).

        :param ToneInput tone_input: JSON, plain text, or HTML input that contains the
        content to be analyzed. For JSON input, provide an object of type `ToneInput`.
        :param bool sentences: Indicates whether the service is to return an analysis of
        each individual sentence in addition to its analysis of the full document. If
        `true` (the default), the service returns results for each sentence.
        :param list[str] tones: **`2017-09-21`:** Deprecated. The service continues to
        accept the parameter for backward-compatibility, but the parameter no longer
        affects the response.
        **`2016-05-19`:** A comma-separated list of tones for which the service is to
        return its analysis of the input; the indicated tones apply both to the full
        document and to individual sentences of the document. You can specify one or more
        of the valid values. Omit the parameter to request results for all three tones.
        :param str content_language: The language of the input text for the request:
        English or French. Regional variants are treated as their parent language; for
        example, `en-US` is interpreted as `en`. The input content must match the
        specified language. Do not submit content that contains both languages. You can
        use different languages for **Content-Language** and **Accept-Language**.
        * **`2017-09-21`:** Accepts `en` or `fr`.
        * **`2016-05-19`:** Accepts only `en`.
        :param str accept_language: The desired language of the response. For
        two-character arguments, regional variants are treated as their parent language;
        for example, `en-US` is interpreted as `en`. You can use different languages for
        **Content-Language** and **Accept-Language**.
        :param str content_type: The type of the input. A character encoding can be
        specified by including a `charset` parameter. For example,
        'text/plain;charset=utf-8'.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse
        """

        if tone_input is None:
            raise ValueError('tone_input must be provided')
        if isinstance(tone_input, ToneInput):
            tone_input = self._convert_model(tone_input, ToneInput)

        headers = {
            'Content-Language': content_language,
            'Accept-Language': accept_language,
            'Content-Type': content_type
        }
        if 'headers' in kwargs:
            headers.update(kwargs.get('headers'))
        sdk_headers = get_sdk_headers('tone_analyzer', 'V3', 'tone')
        headers.update(sdk_headers)

        params = {
            'version': self.version,
            'sentences': sentences,
            'tones': self._convert_list(tones)
        }

        if content_type == 'application/json' and isinstance(tone_input, dict):
            data = json.dumps(tone_input)
        else:
            data = tone_input

        url = '/v3/tone'
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            data=data,
            accept_json=True)
        return response


Instruction:
Can you create a Python 3 function that
analyzes the tone of a customer service and customer support conversations and return a dict containing the most prevalent subset of the utterances.

Response:
def tone_chat(self,
                  utterances,
                  content_language=None,
                  accept_language=None,
                  **kwargs):
        """
        Analyze customer engagement tone.

        Use the customer engagement endpoint to analyze the tone of customer service and
        customer support conversations. For each utterance of a conversation, the method
        reports the most prevalent subset of the following seven tones: sad, frustrated,
        satisfied, excited, polite, impolite, and sympathetic.
        If you submit more than 50 utterances, the service returns a warning for the
        overall content and analyzes only the first 50 utterances. If you submit a single
        utterance that contains more than 500 characters, the service returns an error for
        that utterance and does not analyze the utterance. The request fails if all
        utterances have more than 500 characters. Per the JSON specification, the default
        character encoding for JSON content is effectively always UTF-8.
        **See also:** [Using the customer-engagement
        endpoint](https://cloud.ibm.com/docs/services/tone-analyzer/using-tone-chat.html#using-the-customer-engagement-endpoint).

        :param list[Utterance] utterances: An array of `Utterance` objects that provides
        the input content that the service is to analyze.
        :param str content_language: The language of the input text for the request:
        English or French. Regional variants are treated as their parent language; for
        example, `en-US` is interpreted as `en`. The input content must match the
        specified language. Do not submit content that contains both languages. You can
        use different languages for **Content-Language** and **Accept-Language**.
        * **`2017-09-21`:** Accepts `en` or `fr`.
        * **`2016-05-19`:** Accepts only `en`.
        :param str accept_language: The desired language of the response. For
        two-character arguments, regional variants are treated as their parent language;
        for example, `en-US` is interpreted as `en`. You can use different languages for
        **Content-Language** and **Accept-Language**.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse
        """

        if utterances is None:
            raise ValueError('utterances must be provided')
        utterances = [self._convert_model(x, Utterance) for x in utterances]

        headers = {
            'Content-Language': content_language,
            'Accept-Language': accept_language
        }
        if 'headers' in kwargs:
            headers.update(kwargs.get('headers'))
        sdk_headers = get_sdk_headers('tone_analyzer', 'V3', 'tone_chat')
        headers.update(sdk_headers)

        params = {'version': self.version}

        data = {'utterances': utterances}

        url = '/v3/tone_chat'
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response


Instruction:
Explain what the following Python 3 code does
def _from_dict(cls, _dict):
        args = {}
        if 'tones' in _dict:
            args['tones'] = [
                ToneScore._from_dict(x) for x in (_dict.get('tones'))
            ]
        if 'tone_categories' in _dict:
            args['tone_categories'] = [
                ToneCategory._from_dict(x)
                for x in (_dict.get('tone_categories'))
            ]
        if 'warning' in _dict:
            args['warning'] = _dict.get('warning')
        return cls(**args)

Response: Initialize a DocumentAnalysis object from a json dictionary.


Instruction:
Can you generate the documentation for the following Python 3 function
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'tones') and self.tones is not None:
            _dict['tones'] = [x._to_dict() for x in self.tones]
        if hasattr(self,
                   'tone_categories') and self.tone_categories is not None:
            _dict['tone_categories'] = [
                x._to_dict() for x in self.tone_categories
            ]
        if hasattr(self, 'warning') and self.warning is not None:
            _dict['warning'] = self.warning
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you tell what is the following Python 3 function doing
def _from_dict(cls, _dict):
        args = {}
        if 'sentence_id' in _dict:
            args['sentence_id'] = _dict.get('sentence_id')
        else:
            raise ValueError(
                'Required property \'sentence_id\' not present in SentenceAnalysis JSON'
            )
        if 'text' in _dict:
            args['text'] = _dict.get('text')
        else:
            raise ValueError(
                'Required property \'text\' not present in SentenceAnalysis JSON'
            )
        if 'tones' in _dict:
            args['tones'] = [
                ToneScore._from_dict(x) for x in (_dict.get('tones'))
            ]
        if 'tone_categories' in _dict:
            args['tone_categories'] = [
                ToneCategory._from_dict(x)
                for x in (_dict.get('tone_categories'))
            ]
        if 'input_from' in _dict:
            args['input_from'] = _dict.get('input_from')
        if 'input_to' in _dict:
            args['input_to'] = _dict.get('input_to')
        return cls(**args)

Response: Initialize a SentenceAnalysis object from a json dictionary.


Instruction:
Can you implement a function in Python 3 that
returns a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'sentence_id') and self.sentence_id is not None:
            _dict['sentence_id'] = self.sentence_id
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'tones') and self.tones is not None:
            _dict['tones'] = [x._to_dict() for x in self.tones]
        if hasattr(self,
                   'tone_categories') and self.tone_categories is not None:
            _dict['tone_categories'] = [
                x._to_dict() for x in self.tone_categories
            ]
        if hasattr(self, 'input_from') and self.input_from is not None:
            _dict['input_from'] = self.input_from
        if hasattr(self, 'input_to') and self.input_to is not None:
            _dict['input_to'] = self.input_to
        return _dict


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        if 'document_tone' in _dict:
            args['document_tone'] = DocumentAnalysis._from_dict(
                _dict.get('document_tone'))
        else:
            raise ValueError(
                'Required property \'document_tone\' not present in ToneAnalysis JSON'
            )
        if 'sentences_tone' in _dict:
            args['sentences_tone'] = [
                SentenceAnalysis._from_dict(x)
                for x in (_dict.get('sentences_tone'))
            ]
        return cls(**args)

Response: Initialize a ToneAnalysis object from a json dictionary.


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'document_tone') and self.document_tone is not None:
            _dict['document_tone'] = self.document_tone._to_dict()
        if hasattr(self, 'sentences_tone') and self.sentences_tone is not None:
            _dict['sentences_tone'] = [
                x._to_dict() for x in self.sentences_tone
            ]
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Given the following Python 3 function, write the documentation
def _from_dict(cls, _dict):
        args = {}
        if 'tones' in _dict:
            args['tones'] = [
                ToneScore._from_dict(x) for x in (_dict.get('tones'))
            ]
        else:
            raise ValueError(
                'Required property \'tones\' not present in ToneCategory JSON')
        if 'category_id' in _dict:
            args['category_id'] = _dict.get('category_id')
        else:
            raise ValueError(
                'Required property \'category_id\' not present in ToneCategory JSON'
            )
        if 'category_name' in _dict:
            args['category_name'] = _dict.get('category_name')
        else:
            raise ValueError(
                'Required property \'category_name\' not present in ToneCategory JSON'
            )
        return cls(**args)

Response: Initialize a ToneCategory object from a json dictionary.


Instruction:
Write a Python 3 script to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'tones') and self.tones is not None:
            _dict['tones'] = [x._to_dict() for x in self.tones]
        if hasattr(self, 'category_id') and self.category_id is not None:
            _dict['category_id'] = self.category_id
        if hasattr(self, 'category_name') and self.category_name is not None:
            _dict['category_name'] = self.category_name
        return _dict


Instruction:
Implement a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'score') and self.score is not None:
            _dict['score'] = self.score
        if hasattr(self, 'tone_id') and self.tone_id is not None:
            _dict['tone_id'] = self.tone_id
        if hasattr(self, 'tone_name') and self.tone_name is not None:
            _dict['tone_name'] = self.tone_name
        return _dict


Instruction:
How would you code a function in Python 3 to
return a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'user') and self.user is not None:
            _dict['user'] = self.user
        return _dict


Instruction:
Write a Python 3 function for
initializing a UtteranceAnalyses object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a UtteranceAnalyses object from a json dictionary."""
        args = {}
        if 'utterances_tone' in _dict:
            args['utterances_tone'] = [
                UtteranceAnalysis._from_dict(x)
                for x in (_dict.get('utterances_tone'))
            ]
        else:
            raise ValueError(
                'Required property \'utterances_tone\' not present in UtteranceAnalyses JSON'
            )
        if 'warning' in _dict:
            args['warning'] = _dict.get('warning')
        return cls(**args)


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _to_dict(self):
        _dict = {}
        if hasattr(self,
                   'utterances_tone') and self.utterances_tone is not None:
            _dict['utterances_tone'] = [
                x._to_dict() for x in self.utterances_tone
            ]
        if hasattr(self, 'warning') and self.warning is not None:
            _dict['warning'] = self.warning
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
How would you code a function in Python 3 to
initialize a UtteranceAnalysis object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a UtteranceAnalysis object from a json dictionary."""
        args = {}
        if 'utterance_id' in _dict:
            args['utterance_id'] = _dict.get('utterance_id')
        else:
            raise ValueError(
                'Required property \'utterance_id\' not present in UtteranceAnalysis JSON'
            )
        if 'utterance_text' in _dict:
            args['utterance_text'] = _dict.get('utterance_text')
        else:
            raise ValueError(
                'Required property \'utterance_text\' not present in UtteranceAnalysis JSON'
            )
        if 'tones' in _dict:
            args['tones'] = [
                ToneChatScore._from_dict(x) for x in (_dict.get('tones'))
            ]
        else:
            raise ValueError(
                'Required property \'tones\' not present in UtteranceAnalysis JSON'
            )
        if 'error' in _dict:
            args['error'] = _dict.get('error')
        return cls(**args)


Instruction:
Implement a Python 3 function for
returning a json dictionary representing this model.

Response:
def _to_dict(self):
        """Return a json dictionary representing this model."""
        _dict = {}
        if hasattr(self, 'utterance_id') and self.utterance_id is not None:
            _dict['utterance_id'] = self.utterance_id
        if hasattr(self, 'utterance_text') and self.utterance_text is not None:
            _dict['utterance_text'] = self.utterance_text
        if hasattr(self, 'tones') and self.tones is not None:
            _dict['tones'] = [x._to_dict() for x in self.tones]
        if hasattr(self, 'error') and self.error is not None:
            _dict['error'] = self.error
        return _dict


Instruction:
How would you explain what the following Python 3 function does
def message(self,
                workspace_id,
                input=None,
                intents=None,
                entities=None,
                alternate_intents=None,
                context=None,
                output=None,
                nodes_visited_details=None,
                **kwargs):
        """
        Get response to user input.

        Send user input to a workspace and receive a response.
        There is no rate limit for this operation.

        :param str workspace_id: Unique identifier of the workspace.
        :param MessageInput input: An input object that includes the input text.
        :param list[RuntimeIntent] intents: Intents to use when evaluating the user input.
        Include intents from the previous response to continue using those intents rather
        than trying to recognize intents in the new input.
        :param list[RuntimeEntity] entities: Entities to use when evaluating the message.
        Include entities from the previous response to continue using those entities
        rather than detecting entities in the new input.
        :param bool alternate_intents: Whether to return more than one intent. A value of
        `true` indicates that all matching intents are returned.
        :param Context context: State information for the conversation. To maintain state,
        include the context from the previous response.
        :param OutputData output: An output object that includes the response to the user,
        the dialog nodes that were triggered, and messages from the log.
        :param bool nodes_visited_details: Whether to include additional diagnostic
        information about the dialog nodes that were visited during processing of the
        message.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse
        """

        if workspace_id is None:
            raise ValueError('workspace_id must be provided')
        if input is not None:
            input = self._convert_model(input, MessageInput)
        if intents is not None:
            intents = [self._convert_model(x, RuntimeIntent) for x in intents]
        if entities is not None:
            entities = [self._convert_model(x, RuntimeEntity) for x in entities]
        if context is not None:
            context = self._convert_model(context, Context)
        if output is not None:
            output = self._convert_model(output, OutputData)

        headers = {}
        if 'headers' in kwargs:
            headers.update(kwargs.get('headers'))
        sdk_headers = get_sdk_headers('conversation', 'V1', 'message')
        headers.update(sdk_headers)

        params = {
            'version': self.version,
            'nodes_visited_details': nodes_visited_details
        }

        data = {
            'input': input,
            'intents': intents,
            'entities': entities,
            'alternate_intents': alternate_intents,
            'context': context,
            'output': output
        }

        url = '/v1/workspaces/{0}/message'.format(
            *self._encode_path_vars(workspace_id))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response

Response: This method returns a response to a user input from a workspace and receive a response.


Instruction:
Write a Python 3 function that can
list the workspaces associated with a Watson Assistant service instance.

Response:
def list_workspaces(self,
                        page_limit=None,
                        include_count=None,
                        sort=None,
                        cursor=None,
                        include_audit=None,
                        **kwargs):
        """
        List workspaces.

        List the workspaces associated with a Watson Assistant service instance.
        This operation is limited to 500 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param int page_limit: The number of records to return in each page of results.
        :param bool include_count: Whether to include information about the number of
        records returned.
        :param str sort: The attribute by which returned workspaces will be sorted. To
        reverse the sort order, prefix the value with a minus sign (`-`).
        :param str cursor: A token identifying the page of results to retrieve.
        :param bool include_audit: Whether to include the audit properties (`created` and
        `updated` timestamps) in the response.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse
        """

        headers = {}
        if 'headers' in kwargs:
            headers.update(kwargs.get('headers'))
        sdk_headers = get_sdk_headers('conversation', 'V1', 'list_workspaces')
        headers.update(sdk_headers)

        params = {
            'version': self.version,
            'page_limit': page_limit,
            'include_count': include_count,
            'sort': sort,
            'cursor': cursor,
            'include_audit': include_audit
        }

        url = '/v1/workspaces'
        response = self.request(
            method='GET',
            url=url,
            headers=headers,
            params=params,
            accept_json=True)
        return response