# Copyright 2015 Google Inc.
#
# 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.

require 'date'
require 'google/apis/core/base_service'
require 'google/apis/core/json_representation'
require 'google/apis/core/hashable'
require 'google/apis/errors'

module Google
  module Apis
    module MlV1
      
      # Represents a version of the model.
      # Each version is a trained model deployed in the cloud, ready to handle
      # prediction requests. A model can have multiple versions. You can get
      # information about all of the versions of a given model by calling
      # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.
      # versions/list).
      class GoogleCloudMlV1Version
        include Google::Apis::Core::Hashable
      
        # Optional. The Google Cloud ML runtime version to use for this deployment.
        # If not set, Google Cloud ML will choose a version.
        # Corresponds to the JSON property `runtimeVersion`
        # @return [String]
        attr_accessor :runtime_version
      
        # Output only. The time the version was last used for prediction.
        # Corresponds to the JSON property `lastUseTime`
        # @return [String]
        attr_accessor :last_use_time
      
        # Optional. The description specified for the version when it was created.
        # Corresponds to the JSON property `description`
        # @return [String]
        attr_accessor :description
      
        # Required. The Google Cloud Storage location of the trained model used to
        # create the version. See the
        # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview)
        # for
        # more informaiton.
        # When passing Version to
        # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.
        # versions/create)
        # the model service uses the specified location as the source of the model.
        # Once deployed, the model version is hosted by the prediction service, so
        # this location is useful only as a historical record.
        # Corresponds to the JSON property `deploymentUri`
        # @return [String]
        attr_accessor :deployment_uri
      
        # Output only. If true, this version will be used to handle prediction
        # requests that do not specify a version.
        # You can change the default version by calling
        # [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1/projects.
        # models.versions/setDefault).
        # Corresponds to the JSON property `isDefault`
        # @return [Boolean]
        attr_accessor :is_default
        alias_method :is_default?, :is_default
      
        # Output only. The time the version was created.
        # Corresponds to the JSON property `createTime`
        # @return [String]
        attr_accessor :create_time
      
        # Options for manually scaling a model.
        # Corresponds to the JSON property `manualScaling`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1ManualScaling]
        attr_accessor :manual_scaling
      
        # Required.The name specified for the version when it was created.
        # The version name must be unique within the model it is created in.
        # Corresponds to the JSON property `name`
        # @return [String]
        attr_accessor :name
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @runtime_version = args[:runtime_version] if args.key?(:runtime_version)
          @last_use_time = args[:last_use_time] if args.key?(:last_use_time)
          @description = args[:description] if args.key?(:description)
          @deployment_uri = args[:deployment_uri] if args.key?(:deployment_uri)
          @is_default = args[:is_default] if args.key?(:is_default)
          @create_time = args[:create_time] if args.key?(:create_time)
          @manual_scaling = args[:manual_scaling] if args.key?(:manual_scaling)
          @name = args[:name] if args.key?(:name)
        end
      end
      
      # Represents a single hyperparameter to optimize.
      class GoogleCloudMlV1ParameterSpec
        include Google::Apis::Core::Hashable
      
        # Required if type is `DOUBLE` or `INTEGER`. This field
        # should be unset if type is `CATEGORICAL`. This value should be integers if
        # type is INTEGER.
        # Corresponds to the JSON property `minValue`
        # @return [Float]
        attr_accessor :min_value
      
        # Required if type is `DISCRETE`.
        # A list of feasible points.
        # The list should be in strictly increasing order. For instance, this
        # parameter might have possible settings of 1.5, 2.5, and 4.0. This list
        # should not contain more than 1,000 values.
        # Corresponds to the JSON property `discreteValues`
        # @return [Array<Float>]
        attr_accessor :discrete_values
      
        # Optional. How the parameter should be scaled to the hypercube.
        # Leave unset for categorical parameters.
        # Some kind of scaling is strongly recommended for real or integral
        # parameters (e.g., `UNIT_LINEAR_SCALE`).
        # Corresponds to the JSON property `scaleType`
        # @return [String]
        attr_accessor :scale_type
      
        # Required if typeis `DOUBLE` or `INTEGER`. This field
        # should be unset if type is `CATEGORICAL`. This value should be integers if
        # type is `INTEGER`.
        # Corresponds to the JSON property `maxValue`
        # @return [Float]
        attr_accessor :max_value
      
        # Required. The type of the parameter.
        # Corresponds to the JSON property `type`
        # @return [String]
        attr_accessor :type
      
        # Required. The parameter name must be unique amongst all ParameterConfigs in
        # a HyperparameterSpec message. E.g., "learning_rate".
        # Corresponds to the JSON property `parameterName`
        # @return [String]
        attr_accessor :parameter_name
      
        # Required if type is `CATEGORICAL`. The list of possible categories.
        # Corresponds to the JSON property `categoricalValues`
        # @return [Array<String>]
        attr_accessor :categorical_values
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @min_value = args[:min_value] if args.key?(:min_value)
          @discrete_values = args[:discrete_values] if args.key?(:discrete_values)
          @scale_type = args[:scale_type] if args.key?(:scale_type)
          @max_value = args[:max_value] if args.key?(:max_value)
          @type = args[:type] if args.key?(:type)
          @parameter_name = args[:parameter_name] if args.key?(:parameter_name)
          @categorical_values = args[:categorical_values] if args.key?(:categorical_values)
        end
      end
      
      # Represents input parameters for a prediction job.
      class GoogleCloudMlV1PredictionInput
        include Google::Apis::Core::Hashable
      
        # Required. The format of the input data files.
        # Corresponds to the JSON property `dataFormat`
        # @return [String]
        attr_accessor :data_format
      
        # Optional. The Google Cloud ML runtime version to use for this batch
        # prediction. If not set, Google Cloud ML will pick the runtime version used
        # during the CreateVersion request for this model version, or choose the
        # latest stable version when model version information is not available
        # such as when the model is specified by uri.
        # Corresponds to the JSON property `runtimeVersion`
        # @return [String]
        attr_accessor :runtime_version
      
        # Required. The Google Cloud Storage location of the input data files.
        # May contain wildcards.
        # Corresponds to the JSON property `inputPaths`
        # @return [Array<String>]
        attr_accessor :input_paths
      
        # Required. The Google Compute Engine region to run the prediction job in.
        # Corresponds to the JSON property `region`
        # @return [String]
        attr_accessor :region
      
        # Use this field if you want to specify a version of the model to use. The
        # string is formatted the same way as `model_version`, with the addition
        # of the version information:
        # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[
        # YOUR_VERSION]</var>"`
        # Corresponds to the JSON property `versionName`
        # @return [String]
        attr_accessor :version_name
      
        # Use this field if you want to use the default version for the specified
        # model. The string must use the following format:
        # `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
        # Corresponds to the JSON property `modelName`
        # @return [String]
        attr_accessor :model_name
      
        # Required. The output Google Cloud Storage location.
        # Corresponds to the JSON property `outputPath`
        # @return [String]
        attr_accessor :output_path
      
        # Use this field if you want to specify a Google Cloud Storage path for
        # the model to use.
        # Corresponds to the JSON property `uri`
        # @return [String]
        attr_accessor :uri
      
        # Optional. The maximum number of workers to be used for parallel processing.
        # Defaults to 10 if not specified.
        # Corresponds to the JSON property `maxWorkerCount`
        # @return [Fixnum]
        attr_accessor :max_worker_count
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @data_format = args[:data_format] if args.key?(:data_format)
          @runtime_version = args[:runtime_version] if args.key?(:runtime_version)
          @input_paths = args[:input_paths] if args.key?(:input_paths)
          @region = args[:region] if args.key?(:region)
          @version_name = args[:version_name] if args.key?(:version_name)
          @model_name = args[:model_name] if args.key?(:model_name)
          @output_path = args[:output_path] if args.key?(:output_path)
          @uri = args[:uri] if args.key?(:uri)
          @max_worker_count = args[:max_worker_count] if args.key?(:max_worker_count)
        end
      end
      
      # Represents the metadata of the long-running operation.
      class GoogleCloudMlV1beta1OperationMetadata
        include Google::Apis::Core::Hashable
      
        # Indicates whether a request to cancel this operation has been made.
        # Corresponds to the JSON property `isCancellationRequested`
        # @return [Boolean]
        attr_accessor :is_cancellation_requested
        alias_method :is_cancellation_requested?, :is_cancellation_requested
      
        # The time the operation was submitted.
        # Corresponds to the JSON property `createTime`
        # @return [String]
        attr_accessor :create_time
      
        # Contains the name of the model associated with the operation.
        # Corresponds to the JSON property `modelName`
        # @return [String]
        attr_accessor :model_name
      
        # Represents a version of the model.
        # Each version is a trained model deployed in the cloud, ready to handle
        # prediction requests. A model can have multiple versions. You can get
        # information about all of the versions of a given model by calling
        # [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.
        # models.versions/list).
        # Corresponds to the JSON property `version`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1beta1Version]
        attr_accessor :version
      
        # The time operation processing completed.
        # Corresponds to the JSON property `endTime`
        # @return [String]
        attr_accessor :end_time
      
        # The operation type.
        # Corresponds to the JSON property `operationType`
        # @return [String]
        attr_accessor :operation_type
      
        # The time operation processing started.
        # Corresponds to the JSON property `startTime`
        # @return [String]
        attr_accessor :start_time
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @is_cancellation_requested = args[:is_cancellation_requested] if args.key?(:is_cancellation_requested)
          @create_time = args[:create_time] if args.key?(:create_time)
          @model_name = args[:model_name] if args.key?(:model_name)
          @version = args[:version] if args.key?(:version)
          @end_time = args[:end_time] if args.key?(:end_time)
          @operation_type = args[:operation_type] if args.key?(:operation_type)
          @start_time = args[:start_time] if args.key?(:start_time)
        end
      end
      
      # Represents the metadata of the long-running operation.
      class GoogleCloudMlV1OperationMetadata
        include Google::Apis::Core::Hashable
      
        # The time the operation was submitted.
        # Corresponds to the JSON property `createTime`
        # @return [String]
        attr_accessor :create_time
      
        # Contains the name of the model associated with the operation.
        # Corresponds to the JSON property `modelName`
        # @return [String]
        attr_accessor :model_name
      
        # Represents a version of the model.
        # Each version is a trained model deployed in the cloud, ready to handle
        # prediction requests. A model can have multiple versions. You can get
        # information about all of the versions of a given model by calling
        # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.
        # versions/list).
        # Corresponds to the JSON property `version`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
        attr_accessor :version
      
        # The time operation processing completed.
        # Corresponds to the JSON property `endTime`
        # @return [String]
        attr_accessor :end_time
      
        # The operation type.
        # Corresponds to the JSON property `operationType`
        # @return [String]
        attr_accessor :operation_type
      
        # The time operation processing started.
        # Corresponds to the JSON property `startTime`
        # @return [String]
        attr_accessor :start_time
      
        # Indicates whether a request to cancel this operation has been made.
        # Corresponds to the JSON property `isCancellationRequested`
        # @return [Boolean]
        attr_accessor :is_cancellation_requested
        alias_method :is_cancellation_requested?, :is_cancellation_requested
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @create_time = args[:create_time] if args.key?(:create_time)
          @model_name = args[:model_name] if args.key?(:model_name)
          @version = args[:version] if args.key?(:version)
          @end_time = args[:end_time] if args.key?(:end_time)
          @operation_type = args[:operation_type] if args.key?(:operation_type)
          @start_time = args[:start_time] if args.key?(:start_time)
          @is_cancellation_requested = args[:is_cancellation_requested] if args.key?(:is_cancellation_requested)
        end
      end
      
      # Represents a set of hyperparameters to optimize.
      class GoogleCloudMlV1HyperparameterSpec
        include Google::Apis::Core::Hashable
      
        # Required. The type of goal to use for tuning. Available types are
        # `MAXIMIZE` and `MINIMIZE`.
        # Defaults to `MAXIMIZE`.
        # Corresponds to the JSON property `goal`
        # @return [String]
        attr_accessor :goal
      
        # Optional. The Tensorflow summary tag name to use for optimizing trials. For
        # current versions of Tensorflow, this tag name should exactly match what is
        # shown in Tensorboard, including all scopes.  For versions of Tensorflow
        # prior to 0.12, this should be only the tag passed to tf.Summary.
        # By default, "training/hptuning/metric" will be used.
        # Corresponds to the JSON property `hyperparameterMetricTag`
        # @return [String]
        attr_accessor :hyperparameter_metric_tag
      
        # Required. The set of parameters to tune.
        # Corresponds to the JSON property `params`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1ParameterSpec>]
        attr_accessor :params
      
        # Optional. How many training trials should be attempted to optimize
        # the specified hyperparameters.
        # Defaults to one.
        # Corresponds to the JSON property `maxTrials`
        # @return [Fixnum]
        attr_accessor :max_trials
      
        # Optional. The number of training trials to run concurrently.
        # You can reduce the time it takes to perform hyperparameter tuning by adding
        # trials in parallel. However, each trail only benefits from the information
        # gained in completed trials. That means that a trial does not get access to
        # the results of trials running at the same time, which could reduce the
        # quality of the overall optimization.
        # Each trial will use the same scale tier and machine types.
        # Defaults to one.
        # Corresponds to the JSON property `maxParallelTrials`
        # @return [Fixnum]
        attr_accessor :max_parallel_trials
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @goal = args[:goal] if args.key?(:goal)
          @hyperparameter_metric_tag = args[:hyperparameter_metric_tag] if args.key?(:hyperparameter_metric_tag)
          @params = args[:params] if args.key?(:params)
          @max_trials = args[:max_trials] if args.key?(:max_trials)
          @max_parallel_trials = args[:max_parallel_trials] if args.key?(:max_parallel_trials)
        end
      end
      
      # Response message for the ListJobs method.
      class GoogleCloudMlV1ListJobsResponse
        include Google::Apis::Core::Hashable
      
        # The list of jobs.
        # Corresponds to the JSON property `jobs`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Job>]
        attr_accessor :jobs
      
        # Optional. Pass this token as the `page_token` field of the request for a
        # subsequent call.
        # Corresponds to the JSON property `nextPageToken`
        # @return [String]
        attr_accessor :next_page_token
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @jobs = args[:jobs] if args.key?(:jobs)
          @next_page_token = args[:next_page_token] if args.key?(:next_page_token)
        end
      end
      
      # Request message for the SetDefaultVersion request.
      class GoogleCloudMlV1SetDefaultVersionRequest
        include Google::Apis::Core::Hashable
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
        end
      end
      
      # This resource represents a long-running operation that is the result of a
      # network API call.
      class GoogleLongrunningOperation
        include Google::Apis::Core::Hashable
      
        # The `Status` type defines a logical error model that is suitable for different
        # programming environments, including REST APIs and RPC APIs. It is used by
        # [gRPC](https://github.com/grpc). The error model is designed to be:
        # - Simple to use and understand for most users
        # - Flexible enough to meet unexpected needs
        # # Overview
        # The `Status` message contains three pieces of data: error code, error message,
        # and error details. The error code should be an enum value of
        # google.rpc.Code, but it may accept additional error codes if needed.  The
        # error message should be a developer-facing English message that helps
        # developers *understand* and *resolve* the error. If a localized user-facing
        # error message is needed, put the localized message in the error details or
        # localize it in the client. The optional error details may contain arbitrary
        # information about the error. There is a predefined set of error detail types
        # in the package `google.rpc` which can be used for common error conditions.
        # # Language mapping
        # The `Status` message is the logical representation of the error model, but it
        # is not necessarily the actual wire format. When the `Status` message is
        # exposed in different client libraries and different wire protocols, it can be
        # mapped differently. For example, it will likely be mapped to some exceptions
        # in Java, but more likely mapped to some error codes in C.
        # # Other uses
        # The error model and the `Status` message can be used in a variety of
        # environments, either with or without APIs, to provide a
        # consistent developer experience across different environments.
        # Example uses of this error model include:
        # - Partial errors. If a service needs to return partial errors to the client,
        # it may embed the `Status` in the normal response to indicate the partial
        # errors.
        # - Workflow errors. A typical workflow has multiple steps. Each step may
        # have a `Status` message for error reporting purpose.
        # - Batch operations. If a client uses batch request and batch response, the
        # `Status` message should be used directly inside batch response, one for
        # each error sub-response.
        # - Asynchronous operations. If an API call embeds asynchronous operation
        # results in its response, the status of those operations should be
        # represented directly using the `Status` message.
        # - Logging. If some API errors are stored in logs, the message `Status` could
        # be used directly after any stripping needed for security/privacy reasons.
        # Corresponds to the JSON property `error`
        # @return [Google::Apis::MlV1::GoogleRpcStatus]
        attr_accessor :error
      
        # Service-specific metadata associated with the operation.  It typically
        # contains progress information and common metadata such as create time.
        # Some services might not provide such metadata.  Any method that returns a
        # long-running operation should document the metadata type, if any.
        # Corresponds to the JSON property `metadata`
        # @return [Hash<String,Object>]
        attr_accessor :metadata
      
        # If the value is `false`, it means the operation is still in progress.
        # If true, the operation is completed, and either `error` or `response` is
        # available.
        # Corresponds to the JSON property `done`
        # @return [Boolean]
        attr_accessor :done
        alias_method :done?, :done
      
        # The normal response of the operation in case of success.  If the original
        # method returns no data on success, such as `Delete`, the response is
        # `google.protobuf.Empty`.  If the original method is standard
        # `Get`/`Create`/`Update`, the response should be the resource.  For other
        # methods, the response should have the type `XxxResponse`, where `Xxx`
        # is the original method name.  For example, if the original method name
        # is `TakeSnapshot()`, the inferred response type is
        # `TakeSnapshotResponse`.
        # Corresponds to the JSON property `response`
        # @return [Hash<String,Object>]
        attr_accessor :response
      
        # The server-assigned name, which is only unique within the same service that
        # originally returns it. If you use the default HTTP mapping, the
        # `name` should have the format of `operations/some/unique/name`.
        # Corresponds to the JSON property `name`
        # @return [String]
        attr_accessor :name
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @error = args[:error] if args.key?(:error)
          @metadata = args[:metadata] if args.key?(:metadata)
          @done = args[:done] if args.key?(:done)
          @response = args[:response] if args.key?(:response)
          @name = args[:name] if args.key?(:name)
        end
      end
      
      # Represents a machine learning solution.
      # A model can have multiple versions, each of which is a deployed, trained
      # model ready to receive prediction requests. The model itself is just a
      # container.
      class GoogleCloudMlV1Model
        include Google::Apis::Core::Hashable
      
        # Optional. The description specified for the model when it was created.
        # Corresponds to the JSON property `description`
        # @return [String]
        attr_accessor :description
      
        # Optional. If true, enables StackDriver Logging for online prediction.
        # Default is false.
        # Corresponds to the JSON property `onlinePredictionLogging`
        # @return [Boolean]
        attr_accessor :online_prediction_logging
        alias_method :online_prediction_logging?, :online_prediction_logging
      
        # Represents a version of the model.
        # Each version is a trained model deployed in the cloud, ready to handle
        # prediction requests. A model can have multiple versions. You can get
        # information about all of the versions of a given model by calling
        # [projects.models.versions.list](/ml-engine/reference/rest/v1/projects.models.
        # versions/list).
        # Corresponds to the JSON property `defaultVersion`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1Version]
        attr_accessor :default_version
      
        # Optional. The list of regions where the model is going to be deployed.
        # Currently only one region per model is supported.
        # Defaults to 'us-central1' if nothing is set.
        # Note:
        # *   No matter where a model is deployed, it can always be accessed by
        # users from anywhere, both for online and batch prediction.
        # *   The region for a batch prediction job is set by the region field when
        # submitting the batch prediction job and does not take its value from
        # this field.
        # Corresponds to the JSON property `regions`
        # @return [Array<String>]
        attr_accessor :regions
      
        # Required. The name specified for the model when it was created.
        # The model name must be unique within the project it is created in.
        # Corresponds to the JSON property `name`
        # @return [String]
        attr_accessor :name
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @description = args[:description] if args.key?(:description)
          @online_prediction_logging = args[:online_prediction_logging] if args.key?(:online_prediction_logging)
          @default_version = args[:default_version] if args.key?(:default_version)
          @regions = args[:regions] if args.key?(:regions)
          @name = args[:name] if args.key?(:name)
        end
      end
      
      # A generic empty message that you can re-use to avoid defining duplicated
      # empty messages in your APIs. A typical example is to use it as the request
      # or the response type of an API method. For instance:
      # service Foo `
      # rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty);
      # `
      # The JSON representation for `Empty` is empty JSON object ````.
      class GoogleProtobufEmpty
        include Google::Apis::Core::Hashable
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
        end
      end
      
      # Response message for the ListVersions method.
      class GoogleCloudMlV1ListVersionsResponse
        include Google::Apis::Core::Hashable
      
        # The list of versions.
        # Corresponds to the JSON property `versions`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Version>]
        attr_accessor :versions
      
        # Optional. Pass this token as the `page_token` field of the request for a
        # subsequent call.
        # Corresponds to the JSON property `nextPageToken`
        # @return [String]
        attr_accessor :next_page_token
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @versions = args[:versions] if args.key?(:versions)
          @next_page_token = args[:next_page_token] if args.key?(:next_page_token)
        end
      end
      
      # Request message for the CancelJob method.
      class GoogleCloudMlV1CancelJobRequest
        include Google::Apis::Core::Hashable
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
        end
      end
      
      # Options for manually scaling a model.
      class GoogleCloudMlV1beta1ManualScaling
        include Google::Apis::Core::Hashable
      
        # The number of nodes to allocate for this model. These nodes are always up,
        # starting from the time the model is deployed, so the cost of operating
        # this model will be proportional to nodes * number of hours since
        # deployment.
        # Corresponds to the JSON property `nodes`
        # @return [Fixnum]
        attr_accessor :nodes
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @nodes = args[:nodes] if args.key?(:nodes)
        end
      end
      
      # The `Status` type defines a logical error model that is suitable for different
      # programming environments, including REST APIs and RPC APIs. It is used by
      # [gRPC](https://github.com/grpc). The error model is designed to be:
      # - Simple to use and understand for most users
      # - Flexible enough to meet unexpected needs
      # # Overview
      # The `Status` message contains three pieces of data: error code, error message,
      # and error details. The error code should be an enum value of
      # google.rpc.Code, but it may accept additional error codes if needed.  The
      # error message should be a developer-facing English message that helps
      # developers *understand* and *resolve* the error. If a localized user-facing
      # error message is needed, put the localized message in the error details or
      # localize it in the client. The optional error details may contain arbitrary
      # information about the error. There is a predefined set of error detail types
      # in the package `google.rpc` which can be used for common error conditions.
      # # Language mapping
      # The `Status` message is the logical representation of the error model, but it
      # is not necessarily the actual wire format. When the `Status` message is
      # exposed in different client libraries and different wire protocols, it can be
      # mapped differently. For example, it will likely be mapped to some exceptions
      # in Java, but more likely mapped to some error codes in C.
      # # Other uses
      # The error model and the `Status` message can be used in a variety of
      # environments, either with or without APIs, to provide a
      # consistent developer experience across different environments.
      # Example uses of this error model include:
      # - Partial errors. If a service needs to return partial errors to the client,
      # it may embed the `Status` in the normal response to indicate the partial
      # errors.
      # - Workflow errors. A typical workflow has multiple steps. Each step may
      # have a `Status` message for error reporting purpose.
      # - Batch operations. If a client uses batch request and batch response, the
      # `Status` message should be used directly inside batch response, one for
      # each error sub-response.
      # - Asynchronous operations. If an API call embeds asynchronous operation
      # results in its response, the status of those operations should be
      # represented directly using the `Status` message.
      # - Logging. If some API errors are stored in logs, the message `Status` could
      # be used directly after any stripping needed for security/privacy reasons.
      class GoogleRpcStatus
        include Google::Apis::Core::Hashable
      
        # The status code, which should be an enum value of google.rpc.Code.
        # Corresponds to the JSON property `code`
        # @return [Fixnum]
        attr_accessor :code
      
        # A developer-facing error message, which should be in English. Any
        # user-facing error message should be localized and sent in the
        # google.rpc.Status.details field, or localized by the client.
        # Corresponds to the JSON property `message`
        # @return [String]
        attr_accessor :message
      
        # A list of messages that carry the error details.  There will be a
        # common set of message types for APIs to use.
        # Corresponds to the JSON property `details`
        # @return [Array<Hash<String,Object>>]
        attr_accessor :details
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @code = args[:code] if args.key?(:code)
          @message = args[:message] if args.key?(:message)
          @details = args[:details] if args.key?(:details)
        end
      end
      
      # Response message for the ListModels method.
      class GoogleCloudMlV1ListModelsResponse
        include Google::Apis::Core::Hashable
      
        # Optional. Pass this token as the `page_token` field of the request for a
        # subsequent call.
        # Corresponds to the JSON property `nextPageToken`
        # @return [String]
        attr_accessor :next_page_token
      
        # The list of models.
        # Corresponds to the JSON property `models`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1Model>]
        attr_accessor :models
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @next_page_token = args[:next_page_token] if args.key?(:next_page_token)
          @models = args[:models] if args.key?(:models)
        end
      end
      
      # Represents input parameters for a training job.
      class GoogleCloudMlV1TrainingInput
        include Google::Apis::Core::Hashable
      
        # Represents a set of hyperparameters to optimize.
        # Corresponds to the JSON property `hyperparameters`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterSpec]
        attr_accessor :hyperparameters
      
        # Optional. The number of parameter server replicas to use for the training
        # job. Each replica in the cluster will be of the type specified in
        # `parameter_server_type`.
        # This value can only be used when `scale_tier` is set to `CUSTOM`.If you
        # set this value, you must also set `parameter_server_type`.
        # Corresponds to the JSON property `parameterServerCount`
        # @return [Fixnum]
        attr_accessor :parameter_server_count
      
        # Required. The Google Cloud Storage location of the packages with
        # the training program and any additional dependencies.
        # Corresponds to the JSON property `packageUris`
        # @return [Array<String>]
        attr_accessor :package_uris
      
        # Optional. The number of worker replicas to use for the training job. Each
        # replica in the cluster will be of the type specified in `worker_type`.
        # This value can only be used when `scale_tier` is set to `CUSTOM`. If you
        # set this value, you must also set `worker_type`.
        # Corresponds to the JSON property `workerCount`
        # @return [Fixnum]
        attr_accessor :worker_count
      
        # Optional. Specifies the type of virtual machine to use for your training
        # job's master worker.
        # The following types are supported:
        # <dl>
        # <dt>standard</dt>
        # <dd>
        # A basic machine configuration suitable for training simple models with
        # small to moderate datasets.
        # </dd>
        # <dt>large_model</dt>
        # <dd>
        # A machine with a lot of memory, specially suited for parameter servers
        # when your model is large (having many hidden layers or layers with very
        # large numbers of nodes).
        # </dd>
        # <dt>complex_model_s</dt>
        # <dd>
        # A machine suitable for the master and workers of the cluster when your
        # model requires more computation than the standard machine can handle
        # satisfactorily.
        # </dd>
        # <dt>complex_model_m</dt>
        # <dd>
        # A machine with roughly twice the number of cores and roughly double the
        # memory of <code suppresswarning="true">complex_model_s</code>.
        # </dd>
        # <dt>complex_model_l</dt>
        # <dd>
        # A machine with roughly twice the number of cores and roughly double the
        # memory of <code suppresswarning="true">complex_model_m</code>.
        # </dd>
        # <dt>standard_gpu</dt>
        # <dd>
        # A machine equivalent to <code suppresswarning="true">standard</code> that
        # also includes a
        # <a href="/ml-engine/docs/how-tos/using-gpus">
        # GPU that you can use in your trainer</a>.
        # </dd>
        # <dt>complex_model_m_gpu</dt>
        # <dd>
        # A machine equivalent to
        # <code suppresswarning="true">coplex_model_m</code> that also includes
        # four GPUs.
        # </dd>
        # </dl>
        # You must set this value when `scaleTier` is set to `CUSTOM`.
        # Corresponds to the JSON property `masterType`
        # @return [String]
        attr_accessor :master_type
      
        # Optional. The Google Cloud ML runtime version to use for training.  If not
        # set, Google Cloud ML will choose the latest stable version.
        # Corresponds to the JSON property `runtimeVersion`
        # @return [String]
        attr_accessor :runtime_version
      
        # Required. The Python module name to run after installing the packages.
        # Corresponds to the JSON property `pythonModule`
        # @return [String]
        attr_accessor :python_module
      
        # Optional. Specifies the type of virtual machine to use for your training
        # job's worker nodes.
        # The supported values are the same as those described in the entry for
        # `masterType`.
        # This value must be present when `scaleTier` is set to `CUSTOM` and
        # `workerCount` is greater than zero.
        # Corresponds to the JSON property `workerType`
        # @return [String]
        attr_accessor :worker_type
      
        # Optional. Command line arguments to pass to the program.
        # Corresponds to the JSON property `args`
        # @return [Array<String>]
        attr_accessor :args
      
        # Required. The Google Compute Engine region to run the training job in.
        # Corresponds to the JSON property `region`
        # @return [String]
        attr_accessor :region
      
        # Optional. Specifies the type of virtual machine to use for your training
        # job's parameter server.
        # The supported values are the same as those described in the entry for
        # `master_type`.
        # This value must be present when `scaleTier` is set to `CUSTOM` and
        # `parameter_server_count` is greater than zero.
        # Corresponds to the JSON property `parameterServerType`
        # @return [String]
        attr_accessor :parameter_server_type
      
        # Required. Specifies the machine types, the number of replicas for workers
        # and parameter servers.
        # Corresponds to the JSON property `scaleTier`
        # @return [String]
        attr_accessor :scale_tier
      
        # Optional. A Google Cloud Storage path in which to store training outputs
        # and other data needed for training. This path is passed to your TensorFlow
        # program as the 'job_dir' command-line argument. The benefit of specifying
        # this field is that Cloud ML validates the path for use in training.
        # Corresponds to the JSON property `jobDir`
        # @return [String]
        attr_accessor :job_dir
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
          @parameter_server_count = args[:parameter_server_count] if args.key?(:parameter_server_count)
          @package_uris = args[:package_uris] if args.key?(:package_uris)
          @worker_count = args[:worker_count] if args.key?(:worker_count)
          @master_type = args[:master_type] if args.key?(:master_type)
          @runtime_version = args[:runtime_version] if args.key?(:runtime_version)
          @python_module = args[:python_module] if args.key?(:python_module)
          @worker_type = args[:worker_type] if args.key?(:worker_type)
          @args = args[:args] if args.key?(:args)
          @region = args[:region] if args.key?(:region)
          @parameter_server_type = args[:parameter_server_type] if args.key?(:parameter_server_type)
          @scale_tier = args[:scale_tier] if args.key?(:scale_tier)
          @job_dir = args[:job_dir] if args.key?(:job_dir)
        end
      end
      
      # Represents a training or prediction job.
      class GoogleCloudMlV1Job
        include Google::Apis::Core::Hashable
      
        # Represents results of a prediction job.
        # Corresponds to the JSON property `predictionOutput`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionOutput]
        attr_accessor :prediction_output
      
        # Represents results of a training job. Output only.
        # Corresponds to the JSON property `trainingOutput`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingOutput]
        attr_accessor :training_output
      
        # Output only. When the job was created.
        # Corresponds to the JSON property `createTime`
        # @return [String]
        attr_accessor :create_time
      
        # Represents input parameters for a training job.
        # Corresponds to the JSON property `trainingInput`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1TrainingInput]
        attr_accessor :training_input
      
        # Represents input parameters for a prediction job.
        # Corresponds to the JSON property `predictionInput`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1PredictionInput]
        attr_accessor :prediction_input
      
        # Output only. The detailed state of a job.
        # Corresponds to the JSON property `state`
        # @return [String]
        attr_accessor :state
      
        # Output only. The details of a failure or a cancellation.
        # Corresponds to the JSON property `errorMessage`
        # @return [String]
        attr_accessor :error_message
      
        # Required. The user-specified id of the job.
        # Corresponds to the JSON property `jobId`
        # @return [String]
        attr_accessor :job_id
      
        # Output only. When the job processing was completed.
        # Corresponds to the JSON property `endTime`
        # @return [String]
        attr_accessor :end_time
      
        # Output only. When the job processing was started.
        # Corresponds to the JSON property `startTime`
        # @return [String]
        attr_accessor :start_time
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @prediction_output = args[:prediction_output] if args.key?(:prediction_output)
          @training_output = args[:training_output] if args.key?(:training_output)
          @create_time = args[:create_time] if args.key?(:create_time)
          @training_input = args[:training_input] if args.key?(:training_input)
          @prediction_input = args[:prediction_input] if args.key?(:prediction_input)
          @state = args[:state] if args.key?(:state)
          @error_message = args[:error_message] if args.key?(:error_message)
          @job_id = args[:job_id] if args.key?(:job_id)
          @end_time = args[:end_time] if args.key?(:end_time)
          @start_time = args[:start_time] if args.key?(:start_time)
        end
      end
      
      # Message that represents an arbitrary HTTP body. It should only be used for
      # payload formats that can't be represented as JSON, such as raw binary or
      # an HTML page.
      # This message can be used both in streaming and non-streaming API methods in
      # the request as well as the response.
      # It can be used as a top-level request field, which is convenient if one
      # wants to extract parameters from either the URL or HTTP template into the
      # request fields and also want access to the raw HTTP body.
      # Example:
      # message GetResourceRequest `
      # // A unique request id.
      # string request_id = 1;
      # // The raw HTTP body is bound to this field.
      # google.api.HttpBody http_body = 2;
      # `
      # service ResourceService `
      # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
      # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
      # `
      # Example with streaming methods:
      # service CaldavService `
      # rpc GetCalendar(stream google.api.HttpBody)
      # returns (stream google.api.HttpBody);
      # rpc UpdateCalendar(stream google.api.HttpBody)
      # returns (stream google.api.HttpBody);
      # `
      # Use of this type only changes how the request and response bodies are
      # handled, all other features will continue to work unchanged.
      class GoogleApiHttpBody
        include Google::Apis::Core::Hashable
      
        # HTTP body binary data.
        # Corresponds to the JSON property `data`
        # NOTE: Values are automatically base64 encoded/decoded in the client library.
        # @return [String]
        attr_accessor :data
      
        # The HTTP Content-Type string representing the content type of the body.
        # Corresponds to the JSON property `contentType`
        # @return [String]
        attr_accessor :content_type
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @data = args[:data] if args.key?(:data)
          @content_type = args[:content_type] if args.key?(:content_type)
        end
      end
      
      # Represents a version of the model.
      # Each version is a trained model deployed in the cloud, ready to handle
      # prediction requests. A model can have multiple versions. You can get
      # information about all of the versions of a given model by calling
      # [projects.models.versions.list](/ml-engine/reference/rest/v1beta1/projects.
      # models.versions/list).
      class GoogleCloudMlV1beta1Version
        include Google::Apis::Core::Hashable
      
        # Output only. The time the version was last used for prediction.
        # Corresponds to the JSON property `lastUseTime`
        # @return [String]
        attr_accessor :last_use_time
      
        # Optional. The Google Cloud ML runtime version to use for this deployment.
        # If not set, Google Cloud ML will choose a version.
        # Corresponds to the JSON property `runtimeVersion`
        # @return [String]
        attr_accessor :runtime_version
      
        # Optional. The description specified for the version when it was created.
        # Corresponds to the JSON property `description`
        # @return [String]
        attr_accessor :description
      
        # Required. The Google Cloud Storage location of the trained model used to
        # create the version. See the
        # [overview of model deployment](/ml-engine/docs/concepts/deployment-overview)
        # for
        # more informaiton.
        # When passing Version to
        # [projects.models.versions.create](/ml-engine/reference/rest/v1beta1/projects.
        # models.versions/create)
        # the model service uses the specified location as the source of the model.
        # Once deployed, the model version is hosted by the prediction service, so
        # this location is useful only as a historical record.
        # Corresponds to the JSON property `deploymentUri`
        # @return [String]
        attr_accessor :deployment_uri
      
        # Output only. If true, this version will be used to handle prediction
        # requests that do not specify a version.
        # You can change the default version by calling
        # [projects.methods.versions.setDefault](/ml-engine/reference/rest/v1beta1/
        # projects.models.versions/setDefault).
        # Corresponds to the JSON property `isDefault`
        # @return [Boolean]
        attr_accessor :is_default
        alias_method :is_default?, :is_default
      
        # Output only. The time the version was created.
        # Corresponds to the JSON property `createTime`
        # @return [String]
        attr_accessor :create_time
      
        # Options for manually scaling a model.
        # Corresponds to the JSON property `manualScaling`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1beta1ManualScaling]
        attr_accessor :manual_scaling
      
        # Required.The name specified for the version when it was created.
        # The version name must be unique within the model it is created in.
        # Corresponds to the JSON property `name`
        # @return [String]
        attr_accessor :name
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @last_use_time = args[:last_use_time] if args.key?(:last_use_time)
          @runtime_version = args[:runtime_version] if args.key?(:runtime_version)
          @description = args[:description] if args.key?(:description)
          @deployment_uri = args[:deployment_uri] if args.key?(:deployment_uri)
          @is_default = args[:is_default] if args.key?(:is_default)
          @create_time = args[:create_time] if args.key?(:create_time)
          @manual_scaling = args[:manual_scaling] if args.key?(:manual_scaling)
          @name = args[:name] if args.key?(:name)
        end
      end
      
      # Returns service account information associated with a project.
      class GoogleCloudMlV1GetConfigResponse
        include Google::Apis::Core::Hashable
      
        # The project number for `service_account`.
        # Corresponds to the JSON property `serviceAccountProject`
        # @return [Fixnum]
        attr_accessor :service_account_project
      
        # The service account Cloud ML uses to access resources in the project.
        # Corresponds to the JSON property `serviceAccount`
        # @return [String]
        attr_accessor :service_account
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @service_account_project = args[:service_account_project] if args.key?(:service_account_project)
          @service_account = args[:service_account] if args.key?(:service_account)
        end
      end
      
      # Represents the result of a single hyperparameter tuning trial from a
      # training job. The TrainingOutput object that is returned on successful
      # completion of a training job with hyperparameter tuning includes a list
      # of HyperparameterOutput objects, one for each successful trial.
      class GoogleCloudMlV1HyperparameterOutput
        include Google::Apis::Core::Hashable
      
        # The hyperparameters given to this trial.
        # Corresponds to the JSON property `hyperparameters`
        # @return [Hash<String,String>]
        attr_accessor :hyperparameters
      
        # The trial id for these results.
        # Corresponds to the JSON property `trialId`
        # @return [String]
        attr_accessor :trial_id
      
        # All recorded object metrics for this trial.
        # Corresponds to the JSON property `allMetrics`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutputHyperparameterMetric>]
        attr_accessor :all_metrics
      
        # An observed value of a metric.
        # Corresponds to the JSON property `finalMetric`
        # @return [Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutputHyperparameterMetric]
        attr_accessor :final_metric
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @hyperparameters = args[:hyperparameters] if args.key?(:hyperparameters)
          @trial_id = args[:trial_id] if args.key?(:trial_id)
          @all_metrics = args[:all_metrics] if args.key?(:all_metrics)
          @final_metric = args[:final_metric] if args.key?(:final_metric)
        end
      end
      
      # Represents results of a prediction job.
      class GoogleCloudMlV1PredictionOutput
        include Google::Apis::Core::Hashable
      
        # The output Google Cloud Storage location provided at the job creation time.
        # Corresponds to the JSON property `outputPath`
        # @return [String]
        attr_accessor :output_path
      
        # Node hours used by the batch prediction job.
        # Corresponds to the JSON property `nodeHours`
        # @return [Float]
        attr_accessor :node_hours
      
        # The number of generated predictions.
        # Corresponds to the JSON property `predictionCount`
        # @return [Fixnum]
        attr_accessor :prediction_count
      
        # The number of data instances which resulted in errors.
        # Corresponds to the JSON property `errorCount`
        # @return [Fixnum]
        attr_accessor :error_count
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @output_path = args[:output_path] if args.key?(:output_path)
          @node_hours = args[:node_hours] if args.key?(:node_hours)
          @prediction_count = args[:prediction_count] if args.key?(:prediction_count)
          @error_count = args[:error_count] if args.key?(:error_count)
        end
      end
      
      # The response message for Operations.ListOperations.
      class GoogleLongrunningListOperationsResponse
        include Google::Apis::Core::Hashable
      
        # The standard List next-page token.
        # Corresponds to the JSON property `nextPageToken`
        # @return [String]
        attr_accessor :next_page_token
      
        # A list of operations that matches the specified filter in the request.
        # Corresponds to the JSON property `operations`
        # @return [Array<Google::Apis::MlV1::GoogleLongrunningOperation>]
        attr_accessor :operations
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @next_page_token = args[:next_page_token] if args.key?(:next_page_token)
          @operations = args[:operations] if args.key?(:operations)
        end
      end
      
      # Options for manually scaling a model.
      class GoogleCloudMlV1ManualScaling
        include Google::Apis::Core::Hashable
      
        # The number of nodes to allocate for this model. These nodes are always up,
        # starting from the time the model is deployed, so the cost of operating
        # this model will be proportional to nodes * number of hours since
        # deployment.
        # Corresponds to the JSON property `nodes`
        # @return [Fixnum]
        attr_accessor :nodes
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @nodes = args[:nodes] if args.key?(:nodes)
        end
      end
      
      # Represents results of a training job. Output only.
      class GoogleCloudMlV1TrainingOutput
        include Google::Apis::Core::Hashable
      
        # Results for individual Hyperparameter trials.
        # Only set for hyperparameter tuning jobs.
        # Corresponds to the JSON property `trials`
        # @return [Array<Google::Apis::MlV1::GoogleCloudMlV1HyperparameterOutput>]
        attr_accessor :trials
      
        # The number of hyperparameter tuning trials that completed successfully.
        # Only set for hyperparameter tuning jobs.
        # Corresponds to the JSON property `completedTrialCount`
        # @return [Fixnum]
        attr_accessor :completed_trial_count
      
        # Whether this job is a hyperparameter tuning job.
        # Corresponds to the JSON property `isHyperparameterTuningJob`
        # @return [Boolean]
        attr_accessor :is_hyperparameter_tuning_job
        alias_method :is_hyperparameter_tuning_job?, :is_hyperparameter_tuning_job
      
        # The amount of ML units consumed by the job.
        # Corresponds to the JSON property `consumedMLUnits`
        # @return [Float]
        attr_accessor :consumed_ml_units
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @trials = args[:trials] if args.key?(:trials)
          @completed_trial_count = args[:completed_trial_count] if args.key?(:completed_trial_count)
          @is_hyperparameter_tuning_job = args[:is_hyperparameter_tuning_job] if args.key?(:is_hyperparameter_tuning_job)
          @consumed_ml_units = args[:consumed_ml_units] if args.key?(:consumed_ml_units)
        end
      end
      
      # Request for predictions to be issued against a trained model.
      # The body of the request is a single JSON object with a single top-level
      # field:
      # <dl>
      # <dt>instances</dt>
      # <dd>A JSON array containing values representing the instances to use for
      # prediction.</dd>
      # </dl>
      # The structure of each element of the instances list is determined by your
      # model's input definition. Instances can include named inputs or can contain
      # only unlabeled values.
      # Not all data includes named inputs. Some instances will be simple
      # JSON values (boolean, number, or string). However, instances are often lists
      # of simple values, or complex nested lists. Here are some examples of request
      # bodies:
      # CSV data with each row encoded as a string value:
      # <pre>
      # `"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]`
      # </pre>
      # Plain text:
      # <pre>
      # `"instances": ["the quick brown fox", "la bruja le dio"]`
      # </pre>
      # Sentences encoded as lists of words (vectors of strings):
      # <pre>
      # `
      # "instances": [
      # ["the","quick","brown"],
      # ["la","bruja","le"],
      # ...
      # ]
      # `
      # </pre>
      # Floating point scalar values:
      # <pre>
      # `"instances": [0.0, 1.1, 2.2]`
      # </pre>
      # Vectors of integers:
      # <pre>
      # `
      # "instances": [
      # [0, 1, 2],
      # [3, 4, 5],
      # ...
      # ]
      # `
      # </pre>
      # Tensors (in this case, two-dimensional tensors):
      # <pre>
      # `
      # "instances": [
      # [
      # [0, 1, 2],
      # [3, 4, 5]
      # ],
      # ...
      # ]
      # `
      # </pre>
      # Images can be represented different ways. In this encoding scheme the first
      # two dimensions represent the rows and columns of the image, and the third
      # contains lists (vectors) of the R, G, and B values for each pixel.
      # <pre>
      # `
      # "instances": [
      # [
      # [
      # [138, 30, 66],
      # [130, 20, 56],
      # ...
      # ],
      # [
      # [126, 38, 61],
      # [122, 24, 57],
      # ...
      # ],
      # ...
      # ],
      # ...
      # ]
      # `
      # </pre>
      # JSON strings must be encoded as UTF-8. To send binary data, you must
      # base64-encode the data and mark it as binary. To mark a JSON string
      # as binary, replace it with a JSON object with a single attribute named `b64`:
      # <pre>`"b64": "..."` </pre>
      # For example:
      # Two Serialized tf.Examples (fake data, for illustrative purposes only):
      # <pre>
      # `"instances": [`"b64": "X5ad6u"`, `"b64": "IA9j4nx"`]`
      # </pre>
      # Two JPEG image byte strings (fake data, for illustrative purposes only):
      # <pre>
      # `"instances": [`"b64": "ASa8asdf"`, `"b64": "JLK7ljk3"`]`
      # </pre>
      # If your data includes named references, format each instance as a JSON object
      # with the named references as the keys:
      # JSON input data to be preprocessed:
      # <pre>
      # `
      # "instances": [
      # `
      # "a": 1.0,
      # "b": true,
      # "c": "x"
      # `,
      # `
      # "a": -2.0,
      # "b": false,
      # "c": "y"
      # `
      # ]
      # `
      # </pre>
      # Some models have an underlying TensorFlow graph that accepts multiple input
      # tensors. In this case, you should use the names of JSON name/value pairs to
      # identify the input tensors, as shown in the following exmaples:
      # For a graph with input tensor aliases "tag" (string) and "image"
      # (base64-encoded string):
      # <pre>
      # `
      # "instances": [
      # `
      # "tag": "beach",
      # "image": `"b64": "ASa8asdf"`
      # `,
      # `
      # "tag": "car",
      # "image": `"b64": "JLK7ljk3"`
      # `
      # ]
      # `
      # </pre>
      # For a graph with input tensor aliases "tag" (string) and "image"
      # (3-dimensional array of 8-bit ints):
      # <pre>
      # `
      # "instances": [
      # `
      # "tag": "beach",
      # "image": [
      # [
      # [138, 30, 66],
      # [130, 20, 56],
      # ...
      # ],
      # [
      # [126, 38, 61],
      # [122, 24, 57],
      # ...
      # ],
      # ...
      # ]
      # `,
      # `
      # "tag": "car",
      # "image": [
      # [
      # [255, 0, 102],
      # [255, 0, 97],
      # ...
      # ],
      # [
      # [254, 1, 101],
      # [254, 2, 93],
      # ...
      # ],
      # ...
      # ]
      # `,
      # ...
      # ]
      # `
      # </pre>
      # If the call is successful, the response body will contain one prediction
      # entry per instance in the request body. If prediction fails for any
      # instance, the response body will contain no predictions and will contian
      # a single error entry instead.
      class GoogleCloudMlV1PredictRequest
        include Google::Apis::Core::Hashable
      
        # Message that represents an arbitrary HTTP body. It should only be used for
        # payload formats that can't be represented as JSON, such as raw binary or
        # an HTML page.
        # This message can be used both in streaming and non-streaming API methods in
        # the request as well as the response.
        # It can be used as a top-level request field, which is convenient if one
        # wants to extract parameters from either the URL or HTTP template into the
        # request fields and also want access to the raw HTTP body.
        # Example:
        # message GetResourceRequest `
        # // A unique request id.
        # string request_id = 1;
        # // The raw HTTP body is bound to this field.
        # google.api.HttpBody http_body = 2;
        # `
        # service ResourceService `
        # rpc GetResource(GetResourceRequest) returns (google.api.HttpBody);
        # rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty);
        # `
        # Example with streaming methods:
        # service CaldavService `
        # rpc GetCalendar(stream google.api.HttpBody)
        # returns (stream google.api.HttpBody);
        # rpc UpdateCalendar(stream google.api.HttpBody)
        # returns (stream google.api.HttpBody);
        # `
        # Use of this type only changes how the request and response bodies are
        # handled, all other features will continue to work unchanged.
        # Corresponds to the JSON property `httpBody`
        # @return [Google::Apis::MlV1::GoogleApiHttpBody]
        attr_accessor :http_body
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @http_body = args[:http_body] if args.key?(:http_body)
        end
      end
      
      # An observed value of a metric.
      class GoogleCloudMlV1HyperparameterOutputHyperparameterMetric
        include Google::Apis::Core::Hashable
      
        # The global training step for this metric.
        # Corresponds to the JSON property `trainingStep`
        # @return [Fixnum]
        attr_accessor :training_step
      
        # The objective value at this training step.
        # Corresponds to the JSON property `objectiveValue`
        # @return [Float]
        attr_accessor :objective_value
      
        def initialize(**args)
           update!(**args)
        end
      
        # Update properties of this object
        def update!(**args)
          @training_step = args[:training_step] if args.key?(:training_step)
          @objective_value = args[:objective_value] if args.key?(:objective_value)
        end
      end
    end
  end
end