docs: def add_training_data(self,
                            environment_id,
                            collection_id,
                            natural_language_query=None,
                            filter=None,
                            examples=None,
                            **kwargs):
          """
          Add query to training data.
  
          Adds a query to the training data for this collection. The query can contain a
          filter and natural language query.
  
          :param str environment_id: The ID of the environment.
          :param str collection_id: The ID of the collection.
          :param str natural_language_query: The natural text query for the new training
          query.
          :param str filter: The filter used on the collection before the
          **natural_language_query** is applied.
          :param list[TrainingExample] examples: Array of training examples.
          :param dict headers: A `dict` containing the request headers
          :return: A `DetailedResponse` containing the result, headers and HTTP status code.
          :rtype: DetailedResponse
          """
  
          if environment_id is None:
              raise ValueError('environment_id must be provided')
          if collection_id is None:
              raise ValueError('collection_id must be provided')
          if examples is not None:
              examples = [
                  self._convert_model(x, TrainingExample) for x in examples
              ]
  
          headers = {}
          if 'headers' in kwargs:
              headers.update(kwargs.get('headers'))
          sdk_headers = get_sdk_headers('discovery', 'V1', 'add_training_data')
          headers.update(sdk_headers)
  
          params = {'version': self.version}
  
          data = {
              'natural_language_query': natural_language_query,
              'filter': filter,
              'examples': examples
          }
  
          url = '/v1/environments/{0}/collections/{1}/training_data'.format(
              *self._encode_path_vars(environment_id, collection_id))
          response = self.request(
              method='POST',
              url=url,
              headers=headers,
              params=params,
              json=data,
              accept_json=True)
          return response