Instruction:
How would you explain what the following Python 3 function does
def update_workspace(self,
                         workspace_id,
                         name=None,
                         description=None,
                         language=None,
                         metadata=None,
                         learning_opt_out=None,
                         system_settings=None,
                         intents=None,
                         entities=None,
                         dialog_nodes=None,
                         counterexamples=None,
                         append=None,
                         **kwargs):
        """
        Update workspace.

        Update an existing workspace with new or modified data. You must provide component
        objects defining the content of the updated workspace.
        This operation is limited to 30 request per 30 minutes. For more information, see
        **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str name: The name of the workspace. This string cannot contain carriage
        return, newline, or tab characters, and it must be no longer than 64 characters.
        :param str description: The description of the workspace. This string cannot
        contain carriage return, newline, or tab characters, and it must be no longer than
        128 characters.
        :param str language: The language of the workspace.
        :param dict metadata: Any metadata related to the workspace.
        :param bool learning_opt_out: Whether training data from the workspace (including
        artifacts such as intents and entities) can be used by IBM for general service
        improvements. `true` indicates that workspace training data is not to be used.
        :param WorkspaceSystemSettings system_settings: Global settings for the workspace.
        :param list[CreateIntent] intents: An array of objects defining the intents for
        the workspace.
        :param list[CreateEntity] entities: An array of objects describing the entities
        for the workspace.
        :param list[DialogNode] dialog_nodes: An array of objects describing the dialog
        nodes in the workspace.
        :param list[Counterexample] counterexamples: An array of objects defining input
        examples that have been marked as irrelevant input.
        :param bool append: Whether the new data is to be appended to the existing data in
        the workspace. If **append**=`false`, elements included in the new data completely
        replace the corresponding existing elements, including all subelements. For
        example, if the new data includes **entities** and **append**=`false`, all
        existing entities in the workspace are discarded and replaced with the new
        entities.
        If **append**=`true`, existing elements are preserved, and the new elements are
        added. If any elements in the new data collide with existing elements, the update
        request fails.
        :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 system_settings is not None:
            system_settings = self._convert_model(system_settings,
                                                  WorkspaceSystemSettings)
        if intents is not None:
            intents = [self._convert_model(x, CreateIntent) for x in intents]
        if entities is not None:
            entities = [self._convert_model(x, CreateEntity) for x in entities]
        if dialog_nodes is not None:
            dialog_nodes = [
                self._convert_model(x, DialogNode) for x in dialog_nodes
            ]
        if counterexamples is not None:
            counterexamples = [
                self._convert_model(x, Counterexample) for x in counterexamples
            ]

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

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

        data = {
            'name': name,
            'description': description,
            'language': language,
            'metadata': metadata,
            'learning_opt_out': learning_opt_out,
            'system_settings': system_settings,
            'intents': intents,
            'entities': entities,
            'dialog_nodes': dialog_nodes,
            'counterexamples': counterexamples
        }

        url = '/v1/workspaces/{0}'.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: Update an existing workspace with new or modified data.


Instruction:
Can you generate the documentation for the following Python 3 function
def create_intent(self,
                      workspace_id,
                      intent,
                      description=None,
                      examples=None,
                      **kwargs):
        """
        Create intent.

        Create a new intent.
        This operation is limited to 2000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str intent: The name of the intent. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, underscore, hyphen, and dot
        characters.
        - It cannot begin with the reserved prefix `sys-`.
        - It must be no longer than 128 characters.
        :param str description: The description of the intent. This string cannot contain
        carriage return, newline, or tab characters, and it must be no longer than 128
        characters.
        :param list[Example] examples: An array of user input examples for the intent.
        :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 intent is None:
            raise ValueError('intent must be provided')
        if examples is not None:
            examples = [self._convert_model(x, Example) for x in examples]

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

        params = {'version': self.version}

        data = {
            'intent': intent,
            'description': description,
            'examples': examples
        }

        url = '/v1/workspaces/{0}/intents'.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: Create a new intent.


Instruction:
Can you create a Python 3 function that
updates an existing intent.

Response:
def update_intent(self,
                      workspace_id,
                      intent,
                      new_intent=None,
                      new_description=None,
                      new_examples=None,
                      **kwargs):
        """
        Update intent.

        Update an existing intent with new or modified data. You must provide component
        objects defining the content of the updated intent.
        This operation is limited to 2000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str intent: The intent name.
        :param str new_intent: The name of the intent. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, underscore, hyphen, and dot
        characters.
        - It cannot begin with the reserved prefix `sys-`.
        - It must be no longer than 128 characters.
        :param str new_description: The description of the intent. This string cannot
        contain carriage return, newline, or tab characters, and it must be no longer than
        128 characters.
        :param list[Example] new_examples: An array of user input examples for the intent.
        :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 intent is None:
            raise ValueError('intent must be provided')
        if new_examples is not None:
            new_examples = [
                self._convert_model(x, Example) for x in new_examples
            ]

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

        params = {'version': self.version}

        data = {
            'intent': new_intent,
            'description': new_description,
            'examples': new_examples
        }

        url = '/v1/workspaces/{0}/intents/{1}'.format(
            *self._encode_path_vars(workspace_id, intent))
        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 create_example(self,
                       workspace_id,
                       intent,
                       text,
                       mentions=None,
                       **kwargs):
        """
        Create user input example.

        Add a new user input example to an intent.
        This operation is limited to 1000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str intent: The intent name.
        :param str text: The text of a user input example. This string must conform to the
        following restrictions:
        - It cannot contain carriage return, newline, or tab characters.
        - It cannot consist of only whitespace characters.
        - It must be no longer than 1024 characters.
        :param list[Mention] mentions: An array of contextual entity mentions.
        :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 intent is None:
            raise ValueError('intent must be provided')
        if text is None:
            raise ValueError('text must be provided')
        if mentions is not None:
            mentions = [self._convert_model(x, Mention) for x in mentions]

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

        params = {'version': self.version}

        data = {'text': text, 'mentions': mentions}

        url = '/v1/workspaces/{0}/intents/{1}/examples'.format(
            *self._encode_path_vars(workspace_id, intent))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response

Response: Create a new user input example.


Instruction:
How would you code a function in Python 3 to
create a new entity.

Response:
def create_entity(self,
                      workspace_id,
                      entity,
                      description=None,
                      metadata=None,
                      fuzzy_match=None,
                      values=None,
                      **kwargs):
        """
        Create entity.

        Create a new entity, or enable a system entity.
        This operation is limited to 1000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str entity: The name of the entity. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, underscore, and hyphen characters.
        - It must be no longer than 64 characters.
        If you specify an entity name beginning with the reserved prefix `sys-`, it must
        be the name of a system entity that you want to enable. (Any entity content
        specified with the request is ignored.).
        :param str description: The description of the entity. This string cannot contain
        carriage return, newline, or tab characters, and it must be no longer than 128
        characters.
        :param dict metadata: Any metadata related to the entity.
        :param bool fuzzy_match: Whether to use fuzzy matching for the entity.
        :param list[CreateValue] values: An array of objects describing the entity values.
        :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 entity is None:
            raise ValueError('entity must be provided')
        if values is not None:
            values = [self._convert_model(x, CreateValue) for x in values]

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

        params = {'version': self.version}

        data = {
            'entity': entity,
            'description': description,
            'metadata': metadata,
            'fuzzy_match': fuzzy_match,
            'values': values
        }

        url = '/v1/workspaces/{0}/entities'.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


Instruction:
Explain what the following Python 3 code does
def update_entity(self,
                      workspace_id,
                      entity,
                      new_entity=None,
                      new_description=None,
                      new_metadata=None,
                      new_fuzzy_match=None,
                      new_values=None,
                      **kwargs):
        """
        Update entity.

        Update an existing entity with new or modified data. You must provide component
        objects defining the content of the updated entity.
        This operation is limited to 1000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str entity: The name of the entity.
        :param str new_entity: The name of the entity. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, underscore, and hyphen characters.
        - It cannot begin with the reserved prefix `sys-`.
        - It must be no longer than 64 characters.
        :param str new_description: The description of the entity. This string cannot
        contain carriage return, newline, or tab characters, and it must be no longer than
        128 characters.
        :param dict new_metadata: Any metadata related to the entity.
        :param bool new_fuzzy_match: Whether to use fuzzy matching for the entity.
        :param list[CreateValue] new_values: An array of objects describing the entity
        values.
        :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 entity is None:
            raise ValueError('entity must be provided')
        if new_values is not None:
            new_values = [
                self._convert_model(x, CreateValue) for x in new_values
            ]

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

        params = {'version': self.version}

        data = {
            'entity': new_entity,
            'description': new_description,
            'metadata': new_metadata,
            'fuzzy_match': new_fuzzy_match,
            'values': new_values
        }

        url = '/v1/workspaces/{0}/entities/{1}'.format(
            *self._encode_path_vars(workspace_id, entity))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response

Response: Update an existing entity.


Instruction:
Explain what the following Python 3 code does
def create_value(self,
                     workspace_id,
                     entity,
                     value,
                     metadata=None,
                     value_type=None,
                     synonyms=None,
                     patterns=None,
                     **kwargs):
        """
        Create entity value.

        Create a new value for an entity.
        This operation is limited to 1000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str entity: The name of the entity.
        :param str value: The text of the entity value. This string must conform to the
        following restrictions:
        - It cannot contain carriage return, newline, or tab characters.
        - It cannot consist of only whitespace characters.
        - It must be no longer than 64 characters.
        :param dict metadata: Any metadata related to the entity value.
        :param str value_type: Specifies the type of entity value.
        :param list[str] synonyms: An array of synonyms for the entity value. A value can
        specify either synonyms or patterns (depending on the value type), but not both. A
        synonym must conform to the following resrictions:
        - It cannot contain carriage return, newline, or tab characters.
        - It cannot consist of only whitespace characters.
        - It must be no longer than 64 characters.
        :param list[str] patterns: An array of patterns for the entity value. A value can
        specify either synonyms or patterns (depending on the value type), but not both. A
        pattern is a regular expression no longer than 512 characters. For more
        information about how to specify a pattern, see the
        [documentation](https://cloud.ibm.com/docs/services/assistant/entities.html#entities-create-dictionary-based).
        :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 entity is None:
            raise ValueError('entity must be provided')
        if value is None:
            raise ValueError('value must be provided')

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

        params = {'version': self.version}

        data = {
            'value': value,
            'metadata': metadata,
            'type': value_type,
            'synonyms': synonyms,
            'patterns': patterns
        }

        url = '/v1/workspaces/{0}/entities/{1}/values'.format(
            *self._encode_path_vars(workspace_id, entity))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response

Response: Creates a new value for an entity.


Instruction:
Implement a Python 3 function for
updating entity value. Update an existing entity value with new or modified data. You must provide component objects defining the content of the updated entity value. This operation is limited to 1000 requests per 30 minutes. For more information, see **Rate limiting**. :param str workspace_id: Unique identifier of the workspace. :param str entity: The name of the entity. :param str value: The text of the entity value. :param str new_value: The text of the entity value. This string must conform to the following restrictions: - It cannot contain carriage return, newline, or tab characters. - It cannot consist of only whitespace characters. - It must be no longer than 64 characters. :param dict new_metadata: Any metadata related to the entity value. :param str new_value_type: Specifies the type of entity value. :param list[str] new_synonyms: An array of synonyms for the entity value. A value can specify either synonyms or patterns (depending on the value type), but not both. A synonym must conform to the following resrictions: - It cannot contain carriage return, newline, or tab characters. - It cannot consist of only whitespace characters. - It must be no longer than 64 characters. :param list[str] new_patterns: An array of patterns for the entity value. A value can specify either synonyms or patterns (depending on the value type), but not both. A pattern is a regular expression no longer than 512 characters. For more information about how to specify a pattern, see the [documentation](https://cloud.ibm.com/docs/services/assistant/entities.html#entities-create-dictionary-based). :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse

Response:
def update_value(self,
                     workspace_id,
                     entity,
                     value,
                     new_value=None,
                     new_metadata=None,
                     new_value_type=None,
                     new_synonyms=None,
                     new_patterns=None,
                     **kwargs):
        """
        Update entity value.

        Update an existing entity value with new or modified data. You must provide
        component objects defining the content of the updated entity value.
        This operation is limited to 1000 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str entity: The name of the entity.
        :param str value: The text of the entity value.
        :param str new_value: The text of the entity value. This string must conform to
        the following restrictions:
        - It cannot contain carriage return, newline, or tab characters.
        - It cannot consist of only whitespace characters.
        - It must be no longer than 64 characters.
        :param dict new_metadata: Any metadata related to the entity value.
        :param str new_value_type: Specifies the type of entity value.
        :param list[str] new_synonyms: An array of synonyms for the entity value. A value
        can specify either synonyms or patterns (depending on the value type), but not
        both. A synonym must conform to the following resrictions:
        - It cannot contain carriage return, newline, or tab characters.
        - It cannot consist of only whitespace characters.
        - It must be no longer than 64 characters.
        :param list[str] new_patterns: An array of patterns for the entity value. A value
        can specify either synonyms or patterns (depending on the value type), but not
        both. A pattern is a regular expression no longer than 512 characters. For more
        information about how to specify a pattern, see the
        [documentation](https://cloud.ibm.com/docs/services/assistant/entities.html#entities-create-dictionary-based).
        :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 entity is None:
            raise ValueError('entity must be provided')
        if value is None:
            raise ValueError('value must be provided')

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

        params = {'version': self.version}

        data = {
            'value': new_value,
            'metadata': new_metadata,
            'type': new_value_type,
            'synonyms': new_synonyms,
            'patterns': new_patterns
        }

        url = '/v1/workspaces/{0}/entities/{1}/values/{2}'.format(
            *self._encode_path_vars(workspace_id, entity, value))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response


Instruction:
Given the following Python 3 function, write the documentation
def create_dialog_node(self,
                           workspace_id,
                           dialog_node,
                           description=None,
                           conditions=None,
                           parent=None,
                           previous_sibling=None,
                           output=None,
                           context=None,
                           metadata=None,
                           next_step=None,
                           title=None,
                           node_type=None,
                           event_name=None,
                           variable=None,
                           actions=None,
                           digress_in=None,
                           digress_out=None,
                           digress_out_slots=None,
                           user_label=None,
                           **kwargs):
        """
        Create dialog node.

        Create a new dialog node.
        This operation is limited to 500 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str dialog_node: The dialog node ID. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot
        characters.
        - It must be no longer than 1024 characters.
        :param str description: The description of the dialog node. This string cannot
        contain carriage return, newline, or tab characters, and it must be no longer than
        128 characters.
        :param str conditions: The condition that will trigger the dialog node. This
        string cannot contain carriage return, newline, or tab characters, and it must be
        no longer than 2048 characters.
        :param str parent: The ID of the parent dialog node. This property is omitted if
        the dialog node has no parent.
        :param str previous_sibling: The ID of the previous sibling dialog node. This
        property is omitted if the dialog node has no previous sibling.
        :param DialogNodeOutput output: The output of the dialog node. For more
        information about how to specify dialog node output, see the
        [documentation](https://cloud.ibm.com/docs/services/assistant/dialog-overview.html#dialog-overview-responses).
        :param dict context: The context for the dialog node.
        :param dict metadata: The metadata for the dialog node.
        :param DialogNodeNextStep next_step: The next step to execute following this
        dialog node.
        :param str title: The alias used to identify the dialog node. This string must
        conform to the following restrictions:
        - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot
        characters.
        - It must be no longer than 64 characters.
        :param str node_type: How the dialog node is processed.
        :param str event_name: How an `event_handler` node is processed.
        :param str variable: The location in the dialog context where output is stored.
        :param list[DialogNodeAction] actions: An array of objects describing any actions
        to be invoked by the dialog node.
        :param str digress_in: Whether this top-level dialog node can be digressed into.
        :param str digress_out: Whether this dialog node can be returned to after a
        digression.
        :param str digress_out_slots: Whether the user can digress to top-level nodes
        while filling out slots.
        :param str user_label: A label that can be displayed externally to describe the
        purpose of the node to users. This string must be no longer than 512 characters.
        :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 dialog_node is None:
            raise ValueError('dialog_node must be provided')
        if output is not None:
            output = self._convert_model(output, DialogNodeOutput)
        if next_step is not None:
            next_step = self._convert_model(next_step, DialogNodeNextStep)
        if actions is not None:
            actions = [
                self._convert_model(x, DialogNodeAction) for x in actions
            ]

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

        params = {'version': self.version}

        data = {
            'dialog_node': dialog_node,
            'description': description,
            'conditions': conditions,
            'parent': parent,
            'previous_sibling': previous_sibling,
            'output': output,
            'context': context,
            'metadata': metadata,
            'next_step': next_step,
            'title': title,
            'type': node_type,
            'event_name': event_name,
            'variable': variable,
            'actions': actions,
            'digress_in': digress_in,
            'digress_out': digress_out,
            'digress_out_slots': digress_out_slots,
            'user_label': user_label
        }

        url = '/v1/workspaces/{0}/dialog_nodes'.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 creates a dialog node.


Instruction:
Implement a Python 3 function for
updating dialog node. Update an existing dialog node with new or modified data. This operation is limited to 500 requests per 30 minutes. For more information, see **Rate limiting**. :param str workspace_id: Unique identifier of the workspace. :param str dialog_node: The dialog node ID (for example, `get_order`). :param str new_dialog_node: The dialog node ID. This string must conform to the following restrictions: - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot characters. - It must be no longer than 1024 characters. :param str new_description: The description of the dialog node. This string cannot contain carriage return, newline, or tab characters, and it must be no longer than 128 characters. :param str new_conditions: The condition that will trigger the dialog node. This string cannot contain carriage return, newline, or tab characters, and it must be no longer than 2048 characters. :param str new_parent: The ID of the parent dialog node. This property is omitted if the dialog node has no parent. :param str new_previous_sibling: The ID of the previous sibling dialog node. This property is omitted if the dialog node has no previous sibling. :param DialogNodeOutput new_output: The output of the dialog node. For more information about how to specify dialog node output, see the [documentation](https://cloud.ibm.com/docs/services/assistant/dialog-overview.html#dialog-overview-responses). :param dict new_context: The context for the dialog node. :param dict new_metadata: The metadata for the dialog node. :param DialogNodeNextStep new_next_step: The next step to execute following this dialog node. :param str new_title: The alias used to identify the dialog node. This string must conform to the following restrictions: - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot characters. - It must be no longer than 64 characters. :param str new_node_type: How the dialog node is processed. :param str new_event_name: How an `event_handler` node is processed. :param str new_variable: The location in the dialog context where output is stored. :param list[DialogNodeAction] new_actions: An array of objects describing any actions to be invoked by the dialog node. :param str new_digress_in: Whether this top-level dialog node can be digressed into. :param str new_digress_out: Whether this dialog node can be returned to after a digression. :param str new_digress_out_slots: Whether the user can digress to top-level nodes while filling out slots. :param str new_user_label: A label that can be displayed externally to describe the purpose of the node to users. This string must be no longer than 512 characters. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse

Response:
def update_dialog_node(self,
                           workspace_id,
                           dialog_node,
                           new_dialog_node=None,
                           new_description=None,
                           new_conditions=None,
                           new_parent=None,
                           new_previous_sibling=None,
                           new_output=None,
                           new_context=None,
                           new_metadata=None,
                           new_next_step=None,
                           new_title=None,
                           new_node_type=None,
                           new_event_name=None,
                           new_variable=None,
                           new_actions=None,
                           new_digress_in=None,
                           new_digress_out=None,
                           new_digress_out_slots=None,
                           new_user_label=None,
                           **kwargs):
        """
        Update dialog node.

        Update an existing dialog node with new or modified data.
        This operation is limited to 500 requests per 30 minutes. For more information,
        see **Rate limiting**.

        :param str workspace_id: Unique identifier of the workspace.
        :param str dialog_node: The dialog node ID (for example, `get_order`).
        :param str new_dialog_node: The dialog node ID. This string must conform to the
        following restrictions:
        - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot
        characters.
        - It must be no longer than 1024 characters.
        :param str new_description: The description of the dialog node. This string cannot
        contain carriage return, newline, or tab characters, and it must be no longer than
        128 characters.
        :param str new_conditions: The condition that will trigger the dialog node. This
        string cannot contain carriage return, newline, or tab characters, and it must be
        no longer than 2048 characters.
        :param str new_parent: The ID of the parent dialog node. This property is omitted
        if the dialog node has no parent.
        :param str new_previous_sibling: The ID of the previous sibling dialog node. This
        property is omitted if the dialog node has no previous sibling.
        :param DialogNodeOutput new_output: The output of the dialog node. For more
        information about how to specify dialog node output, see the
        [documentation](https://cloud.ibm.com/docs/services/assistant/dialog-overview.html#dialog-overview-responses).
        :param dict new_context: The context for the dialog node.
        :param dict new_metadata: The metadata for the dialog node.
        :param DialogNodeNextStep new_next_step: The next step to execute following this
        dialog node.
        :param str new_title: The alias used to identify the dialog node. This string must
        conform to the following restrictions:
        - It can contain only Unicode alphanumeric, space, underscore, hyphen, and dot
        characters.
        - It must be no longer than 64 characters.
        :param str new_node_type: How the dialog node is processed.
        :param str new_event_name: How an `event_handler` node is processed.
        :param str new_variable: The location in the dialog context where output is
        stored.
        :param list[DialogNodeAction] new_actions: An array of objects describing any
        actions to be invoked by the dialog node.
        :param str new_digress_in: Whether this top-level dialog node can be digressed
        into.
        :param str new_digress_out: Whether this dialog node can be returned to after a
        digression.
        :param str new_digress_out_slots: Whether the user can digress to top-level nodes
        while filling out slots.
        :param str new_user_label: A label that can be displayed externally to describe
        the purpose of the node to users. This string must be no longer than 512
        characters.
        :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 dialog_node is None:
            raise ValueError('dialog_node must be provided')
        if new_output is not None:
            new_output = self._convert_model(new_output, DialogNodeOutput)
        if new_next_step is not None:
            new_next_step = self._convert_model(new_next_step,
                                                DialogNodeNextStep)
        if new_actions is not None:
            new_actions = [
                self._convert_model(x, DialogNodeAction) for x in new_actions
            ]

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

        params = {'version': self.version}

        data = {
            'dialog_node': new_dialog_node,
            'description': new_description,
            'conditions': new_conditions,
            'parent': new_parent,
            'previous_sibling': new_previous_sibling,
            'output': new_output,
            'context': new_context,
            'metadata': new_metadata,
            'next_step': new_next_step,
            'title': new_title,
            'type': new_node_type,
            'event_name': new_event_name,
            'variable': new_variable,
            'actions': new_actions,
            'digress_in': new_digress_in,
            'digress_out': new_digress_out,
            'digress_out_slots': new_digress_out_slots,
            'user_label': new_user_label
        }

        url = '/v1/workspaces/{0}/dialog_nodes/{1}'.format(
            *self._encode_path_vars(workspace_id, dialog_node))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            params=params,
            json=data,
            accept_json=True)
        return response


Instruction:
Can you generate a brief explanation for the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'conversation_id' in _dict:
            args['conversation_id'] = _dict.get('conversation_id')
            del xtra['conversation_id']
        if 'system' in _dict:
            args['system'] = SystemResponse._from_dict(_dict.get('system'))
            del xtra['system']
        if 'metadata' in _dict:
            args['metadata'] = MessageContextMetadata._from_dict(
                _dict.get('metadata'))
            del xtra['metadata']
        args.update(xtra)
        return cls(**args)

Response: Initialize a Context 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,
                   'conversation_id') and self.conversation_id is not None:
            _dict['conversation_id'] = self.conversation_id
        if hasattr(self, 'system') and self.system is not None:
            _dict['system'] = self.system._to_dict()
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata._to_dict()
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict


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

Response: Return a json dictionary representing this model.


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

Response: Return a json dictionary representing this model.


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, 'entity') and self.entity is not None:
            _dict['entity'] = self.entity
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'fuzzy_match') and self.fuzzy_match is not None:
            _dict['fuzzy_match'] = self.fuzzy_match
        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, 'values') and self.values is not None:
            _dict['values'] = [x._to_dict() for x in self.values]
        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, 'intent') and self.intent is not None:
            _dict['intent'] = self.intent
        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, '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, 'value') and self.value is not None:
            _dict['value'] = self.value
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'value_type') and self.value_type is not None:
            _dict['type'] = self.value_type
        if hasattr(self, 'synonyms') and self.synonyms is not None:
            _dict['synonyms'] = self.synonyms
        if hasattr(self, 'patterns') and self.patterns is not None:
            _dict['patterns'] = self.patterns
        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)
        return _dict

Response: Return a json dictionary representing this model.


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

Response:
def _from_dict(cls, _dict):
        """Initialize a DialogNode object from a json dictionary."""
        args = {}
        if 'dialog_node' in _dict:
            args['dialog_node'] = _dict.get('dialog_node')
        else:
            raise ValueError(
                'Required property \'dialog_node\' not present in DialogNode JSON'
            )
        if 'description' in _dict:
            args['description'] = _dict.get('description')
        if 'conditions' in _dict:
            args['conditions'] = _dict.get('conditions')
        if 'parent' in _dict:
            args['parent'] = _dict.get('parent')
        if 'previous_sibling' in _dict:
            args['previous_sibling'] = _dict.get('previous_sibling')
        if 'output' in _dict:
            args['output'] = DialogNodeOutput._from_dict(_dict.get('output'))
        if 'context' in _dict:
            args['context'] = _dict.get('context')
        if 'metadata' in _dict:
            args['metadata'] = _dict.get('metadata')
        if 'next_step' in _dict:
            args['next_step'] = DialogNodeNextStep._from_dict(
                _dict.get('next_step'))
        if 'title' in _dict:
            args['title'] = _dict.get('title')
        if 'type' in _dict or 'node_type' in _dict:
            args['node_type'] = _dict.get('type') or _dict.get('node_type')
        if 'event_name' in _dict:
            args['event_name'] = _dict.get('event_name')
        if 'variable' in _dict:
            args['variable'] = _dict.get('variable')
        if 'actions' in _dict:
            args['actions'] = [
                DialogNodeAction._from_dict(x) for x in (_dict.get('actions'))
            ]
        if 'digress_in' in _dict:
            args['digress_in'] = _dict.get('digress_in')
        if 'digress_out' in _dict:
            args['digress_out'] = _dict.get('digress_out')
        if 'digress_out_slots' in _dict:
            args['digress_out_slots'] = _dict.get('digress_out_slots')
        if 'user_label' in _dict:
            args['user_label'] = _dict.get('user_label')
        if 'disabled' in _dict:
            args['disabled'] = _dict.get('disabled')
        if 'created' in _dict:
            args['created'] = string_to_datetime(_dict.get('created'))
        if 'updated' in _dict:
            args['updated'] = string_to_datetime(_dict.get('updated'))
        return cls(**args)


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'dialog_node') and self.dialog_node is not None:
            _dict['dialog_node'] = self.dialog_node
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self, 'conditions') and self.conditions is not None:
            _dict['conditions'] = self.conditions
        if hasattr(self, 'parent') and self.parent is not None:
            _dict['parent'] = self.parent
        if hasattr(self,
                   'previous_sibling') and self.previous_sibling is not None:
            _dict['previous_sibling'] = self.previous_sibling
        if hasattr(self, 'output') and self.output is not None:
            _dict['output'] = self.output._to_dict()
        if hasattr(self, 'context') and self.context is not None:
            _dict['context'] = self.context
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'next_step') and self.next_step is not None:
            _dict['next_step'] = self.next_step._to_dict()
        if hasattr(self, 'title') and self.title is not None:
            _dict['title'] = self.title
        if hasattr(self, 'node_type') and self.node_type is not None:
            _dict['type'] = self.node_type
        if hasattr(self, 'event_name') and self.event_name is not None:
            _dict['event_name'] = self.event_name
        if hasattr(self, 'variable') and self.variable is not None:
            _dict['variable'] = self.variable
        if hasattr(self, 'actions') and self.actions is not None:
            _dict['actions'] = [x._to_dict() for x in self.actions]
        if hasattr(self, 'digress_in') and self.digress_in is not None:
            _dict['digress_in'] = self.digress_in
        if hasattr(self, 'digress_out') and self.digress_out is not None:
            _dict['digress_out'] = self.digress_out
        if hasattr(self,
                   'digress_out_slots') and self.digress_out_slots is not None:
            _dict['digress_out_slots'] = self.digress_out_slots
        if hasattr(self, 'user_label') and self.user_label is not None:
            _dict['user_label'] = self.user_label
        if hasattr(self, 'disabled') and self.disabled is not None:
            _dict['disabled'] = self.disabled
        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)
        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, 'dialog_nodes') and self.dialog_nodes is not None:
            _dict['dialog_nodes'] = [x._to_dict() for x in self.dialog_nodes]
        if hasattr(self, 'pagination') and self.pagination is not None:
            _dict['pagination'] = self.pagination._to_dict()
        return _dict


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, 'behavior') and self.behavior is not None:
            _dict['behavior'] = self.behavior
        if hasattr(self, 'dialog_node') and self.dialog_node is not None:
            _dict['dialog_node'] = self.dialog_node
        if hasattr(self, 'selector') and self.selector is not None:
            _dict['selector'] = self.selector
        return _dict


Instruction:
Make a summary of the following Python 3 code
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'generic' in _dict:
            args['generic'] = [
                DialogNodeOutputGeneric._from_dict(x)
                for x in (_dict.get('generic'))
            ]
            del xtra['generic']
        if 'modifiers' in _dict:
            args['modifiers'] = DialogNodeOutputModifiers._from_dict(
                _dict.get('modifiers'))
            del xtra['modifiers']
        args.update(xtra)
        return cls(**args)

Response: Initialize a DialogNodeOutput object from a json dictionary.


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, 'generic') and self.generic is not None:
            _dict['generic'] = [x._to_dict() for x in self.generic]
        if hasattr(self, 'modifiers') and self.modifiers is not None:
            _dict['modifiers'] = self.modifiers._to_dict()
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'response_type') and self.response_type is not None:
            _dict['response_type'] = self.response_type
        if hasattr(self, 'values') and self.values is not None:
            _dict['values'] = [x._to_dict() for x in self.values]
        if hasattr(self,
                   'selection_policy') and self.selection_policy is not None:
            _dict['selection_policy'] = self.selection_policy
        if hasattr(self, 'delimiter') and self.delimiter is not None:
            _dict['delimiter'] = self.delimiter
        if hasattr(self, 'time') and self.time is not None:
            _dict['time'] = self.time
        if hasattr(self, 'typing') and self.typing is not None:
            _dict['typing'] = self.typing
        if hasattr(self, 'source') and self.source is not None:
            _dict['source'] = self.source
        if hasattr(self, 'title') and self.title is not None:
            _dict['title'] = self.title
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self, 'preference') and self.preference is not None:
            _dict['preference'] = self.preference
        if hasattr(self, 'options') and self.options is not None:
            _dict['options'] = [x._to_dict() for x in self.options]
        if hasattr(self, 'message_to_human_agent'
                  ) and self.message_to_human_agent is not None:
            _dict['message_to_human_agent'] = self.message_to_human_agent
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Can you tell what is the following Python 3 function doing
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'overwrite') and self.overwrite is not None:
            _dict['overwrite'] = self.overwrite
        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 'response_type' in _dict:
            args['response_type'] = _dict.get('response_type')
        else:
            raise ValueError(
                'Required property \'response_type\' not present in DialogRuntimeResponseGeneric JSON'
            )
        if 'text' in _dict:
            args['text'] = _dict.get('text')
        if 'time' in _dict:
            args['time'] = _dict.get('time')
        if 'typing' in _dict:
            args['typing'] = _dict.get('typing')
        if 'source' in _dict:
            args['source'] = _dict.get('source')
        if 'title' in _dict:
            args['title'] = _dict.get('title')
        if 'description' in _dict:
            args['description'] = _dict.get('description')
        if 'preference' in _dict:
            args['preference'] = _dict.get('preference')
        if 'options' in _dict:
            args['options'] = [
                DialogNodeOutputOptionsElement._from_dict(x)
                for x in (_dict.get('options'))
            ]
        if 'message_to_human_agent' in _dict:
            args['message_to_human_agent'] = _dict.get('message_to_human_agent')
        if 'topic' in _dict:
            args['topic'] = _dict.get('topic')
        if 'dialog_node' in _dict:
            args['dialog_node'] = _dict.get('dialog_node')
        if 'suggestions' in _dict:
            args['suggestions'] = [
                DialogSuggestion._from_dict(x)
                for x in (_dict.get('suggestions'))
            ]
        return cls(**args)

Response: Initialize a DialogRuntimeResponseGeneric object from a json dictionary.


Instruction:
Explain what the following Python 3 code does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'response_type') and self.response_type is not None:
            _dict['response_type'] = self.response_type
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'time') and self.time is not None:
            _dict['time'] = self.time
        if hasattr(self, 'typing') and self.typing is not None:
            _dict['typing'] = self.typing
        if hasattr(self, 'source') and self.source is not None:
            _dict['source'] = self.source
        if hasattr(self, 'title') and self.title is not None:
            _dict['title'] = self.title
        if hasattr(self, 'description') and self.description is not None:
            _dict['description'] = self.description
        if hasattr(self, 'preference') and self.preference is not None:
            _dict['preference'] = self.preference
        if hasattr(self, 'options') and self.options is not None:
            _dict['options'] = [x._to_dict() for x in self.options]
        if hasattr(self, 'message_to_human_agent'
                  ) and self.message_to_human_agent is not None:
            _dict['message_to_human_agent'] = self.message_to_human_agent
        if hasattr(self, 'topic') and self.topic is not None:
            _dict['topic'] = self.topic
        if hasattr(self, 'dialog_node') and self.dialog_node is not None:
            _dict['dialog_node'] = self.dialog_node
        if hasattr(self, 'suggestions') and self.suggestions is not None:
            _dict['suggestions'] = [x._to_dict() for x in self.suggestions]
        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 'input' in _dict:
            args['input'] = MessageInput._from_dict(_dict.get('input'))
        if 'intents' in _dict:
            args['intents'] = [
                RuntimeIntent._from_dict(x) for x in (_dict.get('intents'))
            ]
        if 'entities' in _dict:
            args['entities'] = [
                RuntimeEntity._from_dict(x) for x in (_dict.get('entities'))
            ]
        return cls(**args)

Response: Initialize a DialogSuggestionValue 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, 'input') and self.input is not None:
            _dict['input'] = self.input._to_dict()
        if hasattr(self, 'intents') and self.intents is not None:
            _dict['intents'] = [x._to_dict() for x in self.intents]
        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 tell what is the following Python 3 function doing
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]
        if hasattr(self, 'pagination') and self.pagination is not None:
            _dict['pagination'] = self.pagination._to_dict()
        return _dict

Response: Return a json dictionary representing this model.


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


Instruction:
How would you explain what the following Python 3 function does
def _from_dict(cls, _dict):
        args = {}
        if 'request' in _dict:
            args['request'] = MessageRequest._from_dict(_dict.get('request'))
        else:
            raise ValueError(
                'Required property \'request\' not present in Log JSON')
        if 'response' in _dict:
            args['response'] = MessageResponse._from_dict(_dict.get('response'))
        else:
            raise ValueError(
                'Required property \'response\' not present in Log JSON')
        if 'log_id' in _dict:
            args['log_id'] = _dict.get('log_id')
        else:
            raise ValueError(
                'Required property \'log_id\' not present in Log JSON')
        if 'request_timestamp' in _dict:
            args['request_timestamp'] = _dict.get('request_timestamp')
        else:
            raise ValueError(
                'Required property \'request_timestamp\' not present in Log JSON'
            )
        if 'response_timestamp' in _dict:
            args['response_timestamp'] = _dict.get('response_timestamp')
        else:
            raise ValueError(
                'Required property \'response_timestamp\' not present in Log JSON'
            )
        if 'workspace_id' in _dict:
            args['workspace_id'] = _dict.get('workspace_id')
        else:
            raise ValueError(
                'Required property \'workspace_id\' not present in Log JSON')
        if 'language' in _dict:
            args['language'] = _dict.get('language')
        else:
            raise ValueError(
                'Required property \'language\' not present in Log JSON')
        return cls(**args)

Response: Initialize a Log 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, 'request') and self.request is not None:
            _dict['request'] = self.request._to_dict()
        if hasattr(self, 'response') and self.response is not None:
            _dict['response'] = self.response._to_dict()
        if hasattr(self, 'log_id') and self.log_id is not None:
            _dict['log_id'] = self.log_id
        if hasattr(self,
                   'request_timestamp') and self.request_timestamp is not None:
            _dict['request_timestamp'] = self.request_timestamp
        if hasattr(
                self,
                'response_timestamp') and self.response_timestamp is not None:
            _dict['response_timestamp'] = self.response_timestamp
        if hasattr(self, 'workspace_id') and self.workspace_id is not None:
            _dict['workspace_id'] = self.workspace_id
        if hasattr(self, 'language') and self.language is not None:
            _dict['language'] = self.language
        return _dict

Response: Return a json dictionary representing this model.


Instruction:
Create a Python 3 function for
initializing a LogCollection object from a json dictionary.

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


Instruction:
Given the following Python 3 function, write the documentation
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'logs') and self.logs is not None:
            _dict['logs'] = [x._to_dict() for x in self.logs]
        if hasattr(self, 'pagination') and self.pagination is not None:
            _dict['pagination'] = self.pagination._to_dict()
        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, 'level') and self.level is not None:
            _dict['level'] = self.level
        if hasattr(self, 'msg') and self.msg is not None:
            _dict['msg'] = self.msg
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict


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, 'next_url') and self.next_url is not None:
            _dict['next_url'] = self.next_url
        if hasattr(self, 'matched') and self.matched is not None:
            _dict['matched'] = self.matched
        if hasattr(self, 'next_cursor') and self.next_cursor is not None:
            _dict['next_cursor'] = self.next_cursor
        return _dict


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


Instruction:
Here you have a function in Python 3, explain what it does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        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:
Write a Python 3 function for
initializing a MessageRequest object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a MessageRequest object from a json dictionary."""
        args = {}
        if 'input' in _dict:
            args['input'] = MessageInput._from_dict(_dict.get('input'))
        if 'intents' in _dict:
            args['intents'] = [
                RuntimeIntent._from_dict(x) for x in (_dict.get('intents'))
            ]
        if 'entities' in _dict:
            args['entities'] = [
                RuntimeEntity._from_dict(x) for x in (_dict.get('entities'))
            ]
        if 'alternate_intents' in _dict:
            args['alternate_intents'] = _dict.get('alternate_intents')
        if 'context' in _dict:
            args['context'] = Context._from_dict(_dict.get('context'))
        if 'output' in _dict:
            args['output'] = OutputData._from_dict(_dict.get('output'))
        if 'actions' in _dict:
            args['actions'] = [
                DialogNodeAction._from_dict(x) for x in (_dict.get('actions'))
            ]
        return cls(**args)


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

Response: Return a json dictionary representing this model.


Instruction:
Explain what the following Python 3 code does
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'log_messages' in _dict:
            args['log_messages'] = [
                LogMessage._from_dict(x) for x in (_dict.get('log_messages'))
            ]
            del xtra['log_messages']
        else:
            raise ValueError(
                'Required property \'log_messages\' not present in OutputData JSON'
            )
        if 'text' in _dict:
            args['text'] = _dict.get('text')
            del xtra['text']
        else:
            raise ValueError(
                'Required property \'text\' not present in OutputData JSON')
        if 'generic' in _dict:
            args['generic'] = [
                DialogRuntimeResponseGeneric._from_dict(x)
                for x in (_dict.get('generic'))
            ]
            del xtra['generic']
        if 'nodes_visited' in _dict:
            args['nodes_visited'] = _dict.get('nodes_visited')
            del xtra['nodes_visited']
        if 'nodes_visited_details' in _dict:
            args['nodes_visited_details'] = [
                DialogNodeVisitedDetails._from_dict(x)
                for x in (_dict.get('nodes_visited_details'))
            ]
            del xtra['nodes_visited_details']
        args.update(xtra)
        return cls(**args)

Response: Initialize a OutputData 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, 'log_messages') and self.log_messages is not None:
            _dict['log_messages'] = [x._to_dict() for x in self.log_messages]
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'generic') and self.generic is not None:
            _dict['generic'] = [x._to_dict() for x in self.generic]
        if hasattr(self, 'nodes_visited') and self.nodes_visited is not None:
            _dict['nodes_visited'] = self.nodes_visited
        if hasattr(self, 'nodes_visited_details'
                  ) and self.nodes_visited_details is not None:
            _dict['nodes_visited_details'] = [
                x._to_dict() for x in self.nodes_visited_details
            ]
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict


Instruction:
Can you generate the documentation for the following Python 3 function
def _from_dict(cls, _dict):
        args = {}
        xtra = _dict.copy()
        if 'entity' in _dict:
            args['entity'] = _dict.get('entity')
            del xtra['entity']
        else:
            raise ValueError(
                'Required property \'entity\' not present in RuntimeEntity JSON'
            )
        if 'location' in _dict:
            args['location'] = _dict.get('location')
            del xtra['location']
        else:
            raise ValueError(
                'Required property \'location\' not present in RuntimeEntity JSON'
            )
        if 'value' in _dict:
            args['value'] = _dict.get('value')
            del xtra['value']
        else:
            raise ValueError(
                'Required property \'value\' not present in RuntimeEntity JSON')
        if 'confidence' in _dict:
            args['confidence'] = _dict.get('confidence')
            del xtra['confidence']
        if 'metadata' in _dict:
            args['metadata'] = _dict.get('metadata')
            del xtra['metadata']
        if 'groups' in _dict:
            args['groups'] = [
                CaptureGroup._from_dict(x) for x in (_dict.get('groups'))
            ]
            del xtra['groups']
        args.update(xtra)
        return cls(**args)

Response: Initialize a RuntimeEntity object from a json dictionary.


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, 'entity') and self.entity is not None:
            _dict['entity'] = self.entity
        if hasattr(self, 'location') and self.location is not None:
            _dict['location'] = self.location
        if hasattr(self, 'value') and self.value is not None:
            _dict['value'] = self.value
        if hasattr(self, 'confidence') and self.confidence is not None:
            _dict['confidence'] = self.confidence
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self, 'groups') and self.groups is not None:
            _dict['groups'] = [x._to_dict() for x in self.groups]
        if hasattr(self, '_additionalProperties'):
            for _key in self._additionalProperties:
                _value = getattr(self, _key, None)
                if _value is not None:
                    _dict[_key] = _value
        return _dict


Instruction:
How would you implement a function in Python 3 that
initializes a RuntimeIntent object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a RuntimeIntent object from a json dictionary."""
        args = {}
        xtra = _dict.copy()
        if 'intent' in _dict:
            args['intent'] = _dict.get('intent')
            del xtra['intent']
        else:
            raise ValueError(
                'Required property \'intent\' not present in RuntimeIntent JSON'
            )
        if 'confidence' in _dict:
            args['confidence'] = _dict.get('confidence')
            del xtra['confidence']
        else:
            raise ValueError(
                'Required property \'confidence\' not present in RuntimeIntent JSON'
            )
        args.update(xtra)
        return cls(**args)


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


Instruction:
How would you explain what the following Python 3 function does
def _to_dict(self):
        _dict = {}
        if hasattr(self, 'synonyms') and self.synonyms is not None:
            _dict['synonyms'] = [x._to_dict() for x in self.synonyms]
        if hasattr(self, 'pagination') and self.pagination is not None:
            _dict['pagination'] = self.pagination._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 'values' in _dict:
            args['values'] = [
                Value._from_dict(x) for x in (_dict.get('values'))
            ]
        else:
            raise ValueError(
                'Required property \'values\' not present in ValueCollection JSON'
            )
        if 'pagination' in _dict:
            args['pagination'] = Pagination._from_dict(_dict.get('pagination'))
        else:
            raise ValueError(
                'Required property \'pagination\' not present in ValueCollection JSON'
            )
        return cls(**args)

Response: Initialize a ValueCollection 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, 'values') and self.values is not None:
            _dict['values'] = [x._to_dict() for x in self.values]
        if hasattr(self, 'pagination') and self.pagination is not None:
            _dict['pagination'] = self.pagination._to_dict()
        return _dict


Instruction:
Given the following Python 3 function, write the documentation
def _from_dict(cls, _dict):
        args = {}
        if 'name' in _dict:
            args['name'] = _dict.get('name')
        else:
            raise ValueError(
                'Required property \'name\' not present in Workspace JSON')
        if 'description' in _dict:
            args['description'] = _dict.get('description')
        if 'language' in _dict:
            args['language'] = _dict.get('language')
        else:
            raise ValueError(
                'Required property \'language\' not present in Workspace JSON')
        if 'metadata' in _dict:
            args['metadata'] = _dict.get('metadata')
        if 'learning_opt_out' in _dict:
            args['learning_opt_out'] = _dict.get('learning_opt_out')
        else:
            raise ValueError(
                'Required property \'learning_opt_out\' not present in Workspace JSON'
            )
        if 'system_settings' in _dict:
            args['system_settings'] = WorkspaceSystemSettings._from_dict(
                _dict.get('system_settings'))
        if 'workspace_id' in _dict:
            args['workspace_id'] = _dict.get('workspace_id')
        else:
            raise ValueError(
                'Required property \'workspace_id\' not present in Workspace JSON'
            )
        if 'status' in _dict:
            args['status'] = _dict.get('status')
        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 'intents' in _dict:
            args['intents'] = [
                Intent._from_dict(x) for x in (_dict.get('intents'))
            ]
        if 'entities' in _dict:
            args['entities'] = [
                Entity._from_dict(x) for x in (_dict.get('entities'))
            ]
        if 'dialog_nodes' in _dict:
            args['dialog_nodes'] = [
                DialogNode._from_dict(x) for x in (_dict.get('dialog_nodes'))
            ]
        if 'counterexamples' in _dict:
            args['counterexamples'] = [
                Counterexample._from_dict(x)
                for x in (_dict.get('counterexamples'))
            ]
        return cls(**args)

Response: Initialize a Workspace 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, '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, 'language') and self.language is not None:
            _dict['language'] = self.language
        if hasattr(self, 'metadata') and self.metadata is not None:
            _dict['metadata'] = self.metadata
        if hasattr(self,
                   'learning_opt_out') and self.learning_opt_out is not None:
            _dict['learning_opt_out'] = self.learning_opt_out
        if hasattr(self,
                   'system_settings') and self.system_settings is not None:
            _dict['system_settings'] = self.system_settings._to_dict()
        if hasattr(self, 'workspace_id') and self.workspace_id is not None:
            _dict['workspace_id'] = self.workspace_id
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        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, 'intents') and self.intents is not None:
            _dict['intents'] = [x._to_dict() for x in self.intents]
        if hasattr(self, 'entities') and self.entities is not None:
            _dict['entities'] = [x._to_dict() for x in self.entities]
        if hasattr(self, 'dialog_nodes') and self.dialog_nodes is not None:
            _dict['dialog_nodes'] = [x._to_dict() for x in self.dialog_nodes]
        if hasattr(self,
                   'counterexamples') and self.counterexamples is not None:
            _dict['counterexamples'] = [
                x._to_dict() for x in self.counterexamples
            ]
        return _dict

Response: Return a json dictionary representing this model.


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

Response: Return a json dictionary representing this model.


Instruction:
Write a Python 3 script to
initialize a WorkspaceSystemSettings object from a json dictionary.

Response:
def _from_dict(cls, _dict):
        """Initialize a WorkspaceSystemSettings object from a json dictionary."""
        args = {}
        if 'tooling' in _dict:
            args['tooling'] = WorkspaceSystemSettingsTooling._from_dict(
                _dict.get('tooling'))
        if 'disambiguation' in _dict:
            args[
                'disambiguation'] = WorkspaceSystemSettingsDisambiguation._from_dict(
                    _dict.get('disambiguation'))
        if 'human_agent_assist' in _dict:
            args['human_agent_assist'] = _dict.get('human_agent_assist')
        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, 'tooling') and self.tooling is not None:
            _dict['tooling'] = self.tooling._to_dict()
        if hasattr(self, 'disambiguation') and self.disambiguation is not None:
            _dict['disambiguation'] = self.disambiguation._to_dict()
        if hasattr(
                self,
                'human_agent_assist') and self.human_agent_assist is not None:
            _dict['human_agent_assist'] = self.human_agent_assist
        return _dict


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


Instruction:
How would you explain what the following Python 3 function does
def classify(self, classifier_id, text, **kwargs):

        if classifier_id is None:
            raise ValueError('classifier_id must be provided')
        if text is None:
            raise ValueError('text must be provided')

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

        data = {'text': text}

        url = '/v1/classifiers/{0}/classify'.format(
            *self._encode_path_vars(classifier_id))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            json=data,
            accept_json=True)
        return response

Response:
Classify a phrase.

        Returns label information for the input. The status must be `Available` before you
        can use the classifier to classify text.

        :param str classifier_id: Classifier ID to use.
        :param str text: The submitted phrase. The maximum length is 2048 characters.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse


Instruction:
Write a Python 3 script to
classify multiple phrases. Returns label information for multiple phrases. The status must be `Available` before you can use the classifier to classify text. Note that classifying Japanese texts is a beta feature. :param str classifier_id: Classifier ID to use. :param list[ClassifyInput] collection: The submitted phrases. :param dict headers: A `dict` containing the request headers :return: A `DetailedResponse` containing the result, headers and HTTP status code. :rtype: DetailedResponse

Response:
def classify_collection(self, classifier_id, collection, **kwargs):
        """
        Classify multiple phrases.

        Returns label information for multiple phrases. The status must be `Available`
        before you can use the classifier to classify text.
        Note that classifying Japanese texts is a beta feature.

        :param str classifier_id: Classifier ID to use.
        :param list[ClassifyInput] collection: The submitted phrases.
        :param dict headers: A `dict` containing the request headers
        :return: A `DetailedResponse` containing the result, headers and HTTP status code.
        :rtype: DetailedResponse
        """

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

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

        data = {'collection': collection}

        url = '/v1/classifiers/{0}/classify_collection'.format(
            *self._encode_path_vars(classifier_id))
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            json=data,
            accept_json=True)
        return response


Instruction:
How would you explain what the following Python 3 function does
def create_classifier(self, metadata, training_data, **kwargs):

        if metadata is None:
            raise ValueError('metadata must be provided')
        if training_data is None:
            raise ValueError('training_data must be provided')

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

        form_data = {}
        form_data['training_metadata'] = (None, metadata, 'application/json')
        form_data['training_data'] = (None, training_data, 'text/csv')

        url = '/v1/classifiers'
        response = self.request(
            method='POST',
            url=url,
            headers=headers,
            files=form_data,
            accept_json=True)
        return response

Response: Create a new classifier.


Instruction:
Can you tell what is the following Python 3 function doing
def _from_dict(cls, _dict):
        args = {}
        if 'classifier_id' in _dict:
            args['classifier_id'] = _dict.get('classifier_id')
        if 'url' in _dict:
            args['url'] = _dict.get('url')
        if 'text' in _dict:
            args['text'] = _dict.get('text')
        if 'top_class' in _dict:
            args['top_class'] = _dict.get('top_class')
        if 'classes' in _dict:
            args['classes'] = [
                ClassifiedClass._from_dict(x) for x in (_dict.get('classes'))
            ]
        return cls(**args)

Response: Initialize a Classification 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, 'classifier_id') and self.classifier_id is not None:
            _dict['classifier_id'] = self.classifier_id
        if hasattr(self, 'url') and self.url is not None:
            _dict['url'] = self.url
        if hasattr(self, 'text') and self.text is not None:
            _dict['text'] = self.text
        if hasattr(self, 'top_class') and self.top_class is not None:
            _dict['top_class'] = self.top_class
        if hasattr(self, 'classes') and self.classes is not None:
            _dict['classes'] = [x._to_dict() for x in self.classes]
        return _dict


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

Response: Initialize a ClassificationCollection 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, 'classifier_id') and self.classifier_id is not None:
            _dict['classifier_id'] = self.classifier_id
        if hasattr(self, 'url') and self.url is not None:
            _dict['url'] = self.url
        if hasattr(self, 'collection') and self.collection is not None:
            _dict['collection'] = [x._to_dict() for x in self.collection]
        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, 'confidence') and self.confidence is not None:
            _dict['confidence'] = self.confidence
        if hasattr(self, 'class_name') and self.class_name is not None:
            _dict['class_name'] = self.class_name
        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, 'name') and self.name is not None:
            _dict['name'] = self.name
        if hasattr(self, 'url') and self.url is not None:
            _dict['url'] = self.url
        if hasattr(self, 'status') and self.status is not None:
            _dict['status'] = self.status
        if hasattr(self, 'classifier_id') and self.classifier_id is not None:
            _dict['classifier_id'] = self.classifier_id
        if hasattr(self, 'created') and self.created is not None:
            _dict['created'] = datetime_to_string(self.created)
        if hasattr(
                self,
                'status_description') and self.status_description is not None:
            _dict['status_description'] = self.status_description
        if hasattr(self, 'language') and self.language is not None:
            _dict['language'] = self.language
        return _dict


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

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


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

Response: Return a json dictionary representing this model.