/**
 * Copyright 2015 Google Inc. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
import { AxiosPromise } from 'axios';
import { GoogleApis } from '../..';
import { BodyResponseCallback, GlobalOptions, MethodOptions } from '../../lib/api';
/**
 * Prediction API
 *
 * Lets you access a cloud hosted machine learning service that makes it easy to
 * build smart apps
 *
 * @example
 * const google = require('googleapis');
 * const prediction = google.prediction('v1.6');
 *
 * @namespace prediction
 * @type {Function}
 * @version v1.6
 * @variation v1.6
 * @param {object=} options Options for Prediction
 */
export declare class Prediction {
    _options: GlobalOptions;
    google: GoogleApis;
    root: this;
    hostedmodels: Resource$Hostedmodels;
    trainedmodels: Resource$Trainedmodels;
    constructor(options: GlobalOptions, google: GoogleApis);
    getRoot(): this;
}
export interface Schema$Analyze {
    /**
     * Description of the data the model was trained on.
     */
    dataDescription: any;
    /**
     * List of errors with the data.
     */
    errors: any[];
    /**
     * The unique name for the predictive model.
     */
    id: string;
    /**
     * What kind of resource this is.
     */
    kind: string;
    /**
     * Description of the model.
     */
    modelDescription: any;
    /**
     * A URL to re-request this resource.
     */
    selfLink: string;
}
export interface Schema$Input {
    /**
     * Input to the model for a prediction.
     */
    input: any;
}
export interface Schema$Insert {
    /**
     * The unique name for the predictive model.
     */
    id: string;
    /**
     * Type of predictive model (classification or regression).
     */
    modelType: string;
    /**
     * The Id of the model to be copied over.
     */
    sourceModel: string;
    /**
     * Google storage location of the training data file.
     */
    storageDataLocation: string;
    /**
     * Google storage location of the preprocessing pmml file.
     */
    storagePMMLLocation: string;
    /**
     * Google storage location of the pmml model file.
     */
    storagePMMLModelLocation: string;
    /**
     * Instances to train model on.
     */
    trainingInstances: any[];
    /**
     * A class weighting function, which allows the importance weights for class
     * labels to be specified (Categorical models only).
     */
    utility: any[];
}
export interface Schema$Insert2 {
    /**
     * Insert time of the model (as a RFC 3339 timestamp).
     */
    created: string;
    /**
     * The unique name for the predictive model.
     */
    id: string;
    /**
     * What kind of resource this is.
     */
    kind: string;
    /**
     * Model metadata.
     */
    modelInfo: any;
    /**
     * Type of predictive model (CLASSIFICATION or REGRESSION).
     */
    modelType: string;
    /**
     * A URL to re-request this resource.
     */
    selfLink: string;
    /**
     * Google storage location of the training data file.
     */
    storageDataLocation: string;
    /**
     * Google storage location of the preprocessing pmml file.
     */
    storagePMMLLocation: string;
    /**
     * Google storage location of the pmml model file.
     */
    storagePMMLModelLocation: string;
    /**
     * Training completion time (as a RFC 3339 timestamp).
     */
    trainingComplete: string;
    /**
     * The current status of the training job. This can be one of following:
     * RUNNING; DONE; ERROR; ERROR: TRAINING JOB NOT FOUND
     */
    trainingStatus: string;
}
export interface Schema$List {
    /**
     * List of models.
     */
    items: Schema$Insert2[];
    /**
     * What kind of resource this is.
     */
    kind: string;
    /**
     * Pagination token to fetch the next page, if one exists.
     */
    nextPageToken: string;
    /**
     * A URL to re-request this resource.
     */
    selfLink: string;
}
export interface Schema$Output {
    /**
     * The unique name for the predictive model.
     */
    id: string;
    /**
     * What kind of resource this is.
     */
    kind: string;
    /**
     * The most likely class label (Categorical models only).
     */
    outputLabel: string;
    /**
     * A list of class labels with their estimated probabilities (Categorical
     * models only).
     */
    outputMulti: any[];
    /**
     * The estimated regression value (Regression models only).
     */
    outputValue: string;
    /**
     * A URL to re-request this resource.
     */
    selfLink: string;
}
export interface Schema$Update {
    /**
     * The input features for this instance.
     */
    csvInstance: any[];
    /**
     * The generic output value - could be regression or class label.
     */
    output: string;
}
export declare class Resource$Hostedmodels {
    root: Prediction;
    constructor(root: Prediction);
    getRoot(): Prediction;
    /**
     * prediction.hostedmodels.predict
     * @desc Submit input and request an output against a hosted model.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The name of a hosted model.
     *     hostedModelName: 'my-hosted-model-name',  // TODO: Update placeholder
     * value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.hostedmodels.predict(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.hostedmodels.predict
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.hostedModelName The name of a hosted model.
     * @param {string} params.project The project associated with the model.
     * @param {().Input} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    predict(params?: any, options?: MethodOptions): AxiosPromise<Schema$Output>;
    predict(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Output>, callback?: BodyResponseCallback<Schema$Output>): void;
}
export declare class Resource$Trainedmodels {
    root: Prediction;
    constructor(root: Prediction);
    getRoot(): Prediction;
    /**
     * prediction.trainedmodels.analyze
     * @desc Get analysis of the model and the data the model was trained on.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.analyze(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.analyze
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    analyze(params?: any, options?: MethodOptions): AxiosPromise<Schema$Analyze>;
    analyze(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Analyze>, callback?: BodyResponseCallback<Schema$Analyze>): void;
    /**
     * prediction.trainedmodels.delete
     * @desc Delete a trained model.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.delete(request, function(err) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.delete
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    delete(params?: any, options?: MethodOptions): AxiosPromise<void>;
    delete(params?: any, options?: MethodOptions | BodyResponseCallback<void>, callback?: BodyResponseCallback<void>): void;
    /**
     * prediction.trainedmodels.get
     * @desc Check training status of your model.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.get(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.get
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    get(params?: any, options?: MethodOptions): AxiosPromise<Schema$Insert2>;
    get(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Insert2>, callback?: BodyResponseCallback<Schema$Insert2>): void;
    /**
     * prediction.trainedmodels.insert
     * @desc Train a Prediction API model.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.insert(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.insert
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.project The project associated with the model.
     * @param {().Insert} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    insert(params?: any, options?: MethodOptions): AxiosPromise<Schema$Insert2>;
    insert(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Insert2>, callback?: BodyResponseCallback<Schema$Insert2>): void;
    /**
     * prediction.trainedmodels.list
     * @desc List available models.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     auth: authClient,
     *   };
     *
     *   var handlePage = function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     var itemsPage = response['items'];
     *     if (!itemsPage) {
     *       return;
     *     }
     *     for (var i = 0; i < itemsPage.length; i++) {
     *       // TODO: Change code below to process each resource in `itemsPage`:
     *       console.log(JSON.stringify(itemsPage[i], null, 2));
     *     }
     *
     *     if (response.nextPageToken) {
     *       request.pageToken = response.nextPageToken;
     *       prediction.trainedmodels.list(request, handlePage);
     *     }
     *   };
     *
     *   prediction.trainedmodels.list(request, handlePage);
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.list
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {integer=} params.maxResults Maximum number of results to return.
     * @param {string=} params.pageToken Pagination token.
     * @param {string} params.project The project associated with the model.
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    list(params?: any, options?: MethodOptions): AxiosPromise<Schema$List>;
    list(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$List>, callback?: BodyResponseCallback<Schema$List>): void;
    /**
     * prediction.trainedmodels.predict
     * @desc Submit model id and request a prediction.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.predict(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.predict
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {().Input} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    predict(params?: any, options?: MethodOptions): AxiosPromise<Schema$Output>;
    predict(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Output>, callback?: BodyResponseCallback<Schema$Output>): void;
    /**
     * prediction.trainedmodels.update
     * @desc Add new data to a trained model.
     * @example
     * * // BEFORE RUNNING:
     * // ---------------
     * // 1. If not already done, enable the Prediction API
     * //    and check the quota for your project at
     * //    https://console.developers.google.com/apis/api/prediction
     * // 2. This sample uses Application Default Credentials for authentication.
     * //    If not already done, install the gcloud CLI from
     * //    https://cloud.google.com/sdk and run
     * //    `gcloud beta auth application-default login`.
     * //    For more information, see
     * //
     * https://developers.google.com/identity/protocols/application-default-credentials
     * // 3. Install the Node.js client library by running
     * //    `npm install googleapis --save`
     *
     * var google = require('googleapis');
     * var prediction = google.prediction('v1.6');
     *
     * authorize(function(authClient) {
     *   var request = {
     *     // The project associated with the model.
     *     project: 'my-project',  // TODO: Update placeholder value.
     *
     *     // The unique name for the predictive model.
     *     id: 'my-id',  // TODO: Update placeholder value.
     *
     *     resource: {
     *       // TODO: Add desired properties to the request body. All existing
     * properties
     *       // will be replaced.
     *     },
     *
     *     auth: authClient,
     *   };
     *
     *   prediction.trainedmodels.update(request, function(err, response) {
     *     if (err) {
     *       console.error(err);
     *       return;
     *     }
     *
     *     // TODO: Change code below to process the `response` object:
     *     console.log(JSON.stringify(response, null, 2));
     *   });
     * });
     *
     * function authorize(callback) {
     *   google.auth.getApplicationDefault(function(err, authClient) {
     *     if (err) {
     *       console.error('authentication failed: ', err);
     *       return;
     *     }
     *     if (authClient.createScopedRequired &&
     * authClient.createScopedRequired()) { var scopes =
     * ['https://www.googleapis.com/auth/cloud-platform']; authClient =
     * authClient.createScoped(scopes);
     *     }
     *     callback(authClient);
     *   });
     * }
     * @alias prediction.trainedmodels.update
     * @memberOf! ()
     *
     * @param {object} params Parameters for request
     * @param {string} params.id The unique name for the predictive model.
     * @param {string} params.project The project associated with the model.
     * @param {().Update} params.resource Request body data
     * @param {object} [options] Optionally override request options, such as `url`, `method`, and `encoding`.
     * @param {callback} callback The callback that handles the response.
     * @return {object} Request object
     */
    update(params?: any, options?: MethodOptions): AxiosPromise<Schema$Insert2>;
    update(params?: any, options?: MethodOptions | BodyResponseCallback<Schema$Insert2>, callback?: BodyResponseCallback<Schema$Insert2>): void;
}