import type { MetadataBearer as __MetadataBearer } from "@smithy/types";
import type { GetMLModelInput, GetMLModelOutput } from "../models/models_0";
/**
 * @public
 */
export type { __MetadataBearer };
/**
 * @public
 *
 * The input for {@link GetMLModelCommand}.
 */
export interface GetMLModelCommandInput extends GetMLModelInput {
}
/**
 * @public
 *
 * The output of {@link GetMLModelCommand}.
 */
export interface GetMLModelCommandOutput extends GetMLModelOutput, __MetadataBearer {
}
declare const GetMLModelCommand_base: {
    new (input: GetMLModelCommandInput): import("@smithy/core/client").CommandImpl<GetMLModelCommandInput, GetMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    new (input: GetMLModelCommandInput): import("@smithy/core/client").CommandImpl<GetMLModelCommandInput, GetMLModelCommandOutput, import("..").MachineLearningClientResolvedConfig, import("..").ServiceInputTypes, import("..").ServiceOutputTypes>;
    getEndpointParameterInstructions(): import("@smithy/types").EndpointParameterInstructions;
};
/**
 * <p>Returns an <code>MLModel</code> that includes detailed metadata, data source information, and the current status of the <code>MLModel</code>.</p>
 *         <p>
 *             <code>GetMLModel</code> provides results in normal or verbose format. </p>
 * @example
 * Use a bare-bones client and the command you need to make an API call.
 * ```javascript
 * import { MachineLearningClient, GetMLModelCommand } from "@aws-sdk/client-machine-learning"; // ES Modules import
 * // const { MachineLearningClient, GetMLModelCommand } = require("@aws-sdk/client-machine-learning"); // CommonJS import
 * // import type { MachineLearningClientConfig } from "@aws-sdk/client-machine-learning";
 * const config = {}; // type is MachineLearningClientConfig
 * const client = new MachineLearningClient(config);
 * const input = { // GetMLModelInput
 *   MLModelId: "STRING_VALUE", // required
 *   Verbose: true || false,
 * };
 * const command = new GetMLModelCommand(input);
 * const response = await client.send(command);
 * // { // GetMLModelOutput
 * //   MLModelId: "STRING_VALUE",
 * //   TrainingDataSourceId: "STRING_VALUE",
 * //   CreatedByIamUser: "STRING_VALUE",
 * //   CreatedAt: new Date("TIMESTAMP"),
 * //   LastUpdatedAt: new Date("TIMESTAMP"),
 * //   Name: "STRING_VALUE",
 * //   Status: "PENDING" || "INPROGRESS" || "FAILED" || "COMPLETED" || "DELETED",
 * //   SizeInBytes: Number("long"),
 * //   EndpointInfo: { // RealtimeEndpointInfo
 * //     PeakRequestsPerSecond: Number("int"),
 * //     CreatedAt: new Date("TIMESTAMP"),
 * //     EndpointUrl: "STRING_VALUE",
 * //     EndpointStatus: "NONE" || "READY" || "UPDATING" || "FAILED",
 * //   },
 * //   TrainingParameters: { // TrainingParameters
 * //     "<keys>": "STRING_VALUE",
 * //   },
 * //   InputDataLocationS3: "STRING_VALUE",
 * //   MLModelType: "REGRESSION" || "BINARY" || "MULTICLASS",
 * //   ScoreThreshold: Number("float"),
 * //   ScoreThresholdLastUpdatedAt: new Date("TIMESTAMP"),
 * //   LogUri: "STRING_VALUE",
 * //   Message: "STRING_VALUE",
 * //   ComputeTime: Number("long"),
 * //   FinishedAt: new Date("TIMESTAMP"),
 * //   StartedAt: new Date("TIMESTAMP"),
 * //   Recipe: "STRING_VALUE",
 * //   Schema: "STRING_VALUE",
 * // };
 *
 * ```
 *
 * @param GetMLModelCommandInput - {@link GetMLModelCommandInput}
 * @returns {@link GetMLModelCommandOutput}
 * @see {@link GetMLModelCommandInput} for command's `input` shape.
 * @see {@link GetMLModelCommandOutput} for command's `response` shape.
 * @see {@link MachineLearningClientResolvedConfig | config} for MachineLearningClient's `config` shape.
 *
 * @throws {@link InternalServerException} (server fault)
 *  <p>An error on the server occurred when trying to process a request.</p>
 *
 * @throws {@link InvalidInputException} (client fault)
 *  <p>An error on the client occurred. Typically, the cause is an invalid input value.</p>
 *
 * @throws {@link ResourceNotFoundException} (client fault)
 *  <p>A specified resource cannot be located.</p>
 *
 * @throws {@link MachineLearningServiceException}
 * <p>Base exception class for all service exceptions from MachineLearning service.</p>
 *
 *
 * @public
 */
export declare class GetMLModelCommand extends GetMLModelCommand_base {
    /** @internal type navigation helper, not in runtime. */
    protected static __types: {
        api: {
            input: GetMLModelInput;
            output: GetMLModelOutput;
        };
        sdk: {
            input: GetMLModelCommandInput;
            output: GetMLModelCommandOutput;
        };
    };
}