# AI Platform Training & Prediction API . projects . locations . studies . trials

## Instance Methods

Adds a measurement of the objective metrics to a trial. This measurement is assumed to have been taken before the trial is complete.

Checks whether a trial should stop or not. Returns a long-running operation. When the operation is successful, it will contain a CheckTrialEarlyStoppingStateResponse.

Close httplib2 connections.

Marks a trial as complete.

Adds a user provided trial to a study.

Deletes a trial.

Gets a trial.

Lists the trials associated with a study.

Lists the pareto-optimal trials for multi-objective study or the optimal trials for single-objective study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency

Stops a trial.

Adds one or more trials to a study, with parameter values suggested by AI Platform Vizier. Returns a long-running operation associated with the generation of trial suggestions. When this long-running operation succeeds, it will contain a SuggestTrialsResponse.

### Method Details

`addMeasurement(name, body=None, x__xgafv=None)`

    Adds a measurement of the objective metrics to a trial. This measurement is assumed to have been taken before the trial is complete.

    Args:
      name: string, Required. The trial name. (required)
      body: object, The request body.
        The object takes the form of:

    { # The request message for the AddTrialMeasurement service method.
      "measurement": { # A message representing a measurement. # Required. The measurement to be added to a trial.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

`checkEarlyStoppingState(name, body=None, x__xgafv=None)`

    Checks whether a trial should stop or not. Returns a long-running operation. When the operation is successful, it will contain a CheckTrialEarlyStoppingStateResponse.

    Args:
      name: string, Required. The trial name. (required)
      body: object, The request body.
        The object takes the form of:

    { # The request message for the CheckTrialEarlyStoppingState service method.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # 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.
      "error": { # 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). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # 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.
      },
      "metadata": { # 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.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # 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 be a resource name ending with `operations/{unique_id}`.
      "response": { # The normal, successful response of the operation. 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`.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }

`close()`

    Close httplib2 connections.

`complete(name, body=None, x__xgafv=None)`

    Marks a trial as complete.

    Args:
      name: string, Required. The trial name.metat (required)
      body: object, The request body.
        The object takes the form of:

    { # The request message for the CompleteTrial service method.
      "finalMeasurement": { # A message representing a measurement. # Optional. If provided, it will be used as the completed trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Optional. A human readable reason why the trial was infeasible. This should only be provided if `trial_infeasible` is true.
      "trialInfeasible": True or False, # Optional. True if the trial cannot be run with the given Parameter, and final_measurement will be ignored.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

`create(parent, body=None, x__xgafv=None)`

    Adds a user provided trial to a study.

    Args:
      parent: string, Required. The name of the study that the trial belongs to. (required)
      body: object, The request body.
        The object takes the form of:

    { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

`delete(name, x__xgafv=None)`

    Deletes a trial.

    Args:
      name: string, Required. The trial name. (required)
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # 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); }
    }

`get(name, x__xgafv=None)`

    Gets a trial.

    Args:
      name: string, Required. The trial name. (required)
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

`list(parent, x__xgafv=None)`

    Lists the trials associated with a study.

    Args:
      parent: string, Required. The name of the study that the trial belongs to. (required)
      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The response message for the ListTrials method.
      "trials": [ # The trials associated with the study.
        { # A message representing a trial.
          "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
          "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
          "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
            "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
            "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
              { # A message representing a metric in the measurement.
                "metric": "A String", # Required. Metric name.
                "value": 3.14, # Required. The value for this metric.
              },
            ],
            "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
          },
          "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
          "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
            { # A message representing a measurement.
              "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
              "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
                { # A message representing a metric in the measurement.
                  "metric": "A String", # Required. Metric name.
                  "value": 3.14, # Required. The value for this metric.
                },
              ],
              "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
            },
          ],
          "name": "A String", # Output only. Name of the trial assigned by the service.
          "parameters": [ # The parameters of the trial.
            { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
              "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
              "intValue": "A String", # Must be set if ParameterType is INTEGER
              "parameter": "A String", # The name of the parameter.
              "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
            },
          ],
          "startTime": "A String", # Output only. Time at which the trial was started.
          "state": "A String", # The detailed state of a trial.
          "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
        },
      ],
    }

`listOptimalTrials(parent, body=None, x__xgafv=None)`

    Lists the pareto-optimal trials for multi-objective study or the optimal trials for single-objective study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency

    Args:
      parent: string, Required. The name of the study that the pareto-optimal trial belongs to. (required)
      body: object, The request body.
        The object takes the form of:

    { # The request message for the ListTrials service method.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # The response message for the ListOptimalTrials method.
      "trials": [ # The pareto-optimal trials for multiple objective study or the optimal trial for single objective study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency
        { # A message representing a trial.
          "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
          "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
          "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
            "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
            "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
              { # A message representing a metric in the measurement.
                "metric": "A String", # Required. Metric name.
                "value": 3.14, # Required. The value for this metric.
              },
            ],
            "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
          },
          "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
          "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
            { # A message representing a measurement.
              "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
              "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
                { # A message representing a metric in the measurement.
                  "metric": "A String", # Required. Metric name.
                  "value": 3.14, # Required. The value for this metric.
                },
              ],
              "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
            },
          ],
          "name": "A String", # Output only. Name of the trial assigned by the service.
          "parameters": [ # The parameters of the trial.
            { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
              "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
              "intValue": "A String", # Must be set if ParameterType is INTEGER
              "parameter": "A String", # The name of the parameter.
              "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
            },
          ],
          "startTime": "A String", # Output only. Time at which the trial was started.
          "state": "A String", # The detailed state of a trial.
          "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
        },
      ],
    }

`stop(name, body=None, x__xgafv=None)`

    Stops a trial.

    Args:
      name: string, Required. The trial name. (required)
      body: object, The request body.
        The object takes the form of:

    {
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # A message representing a trial.
      "clientId": "A String", # Output only. The identifier of the client that originally requested this trial.
      "endTime": "A String", # Output only. Time at which the trial's status changed to COMPLETED.
      "finalMeasurement": { # A message representing a measurement. # The final measurement containing the objective value.
        "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
        "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
          { # A message representing a metric in the measurement.
            "metric": "A String", # Required. Metric name.
            "value": 3.14, # Required. The value for this metric.
          },
        ],
        "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
      },
      "infeasibleReason": "A String", # Output only. A human readable string describing why the trial is infeasible. This should only be set if trial_infeasible is true.
      "measurements": [ # A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_time). These are used for early stopping computations.
        { # A message representing a measurement.
          "elapsedTime": "A String", # Output only. Time that the trial has been running at the point of this measurement.
          "metrics": [ # Provides a list of metrics that act as inputs into the objective function.
            { # A message representing a metric in the measurement.
              "metric": "A String", # Required. Metric name.
              "value": 3.14, # Required. The value for this metric.
            },
          ],
          "stepCount": "A String", # The number of steps a machine learning model has been trained for. Must be non-negative.
        },
      ],
      "name": "A String", # Output only. Name of the trial assigned by the service.
      "parameters": [ # The parameters of the trial.
        { # A message representing a parameter to be tuned. Contains the name of the parameter and the suggested value to use for this trial.
          "floatValue": 3.14, # Must be set if ParameterType is DOUBLE or DISCRETE.
          "intValue": "A String", # Must be set if ParameterType is INTEGER
          "parameter": "A String", # The name of the parameter.
          "stringValue": "A String", # Must be set if ParameterTypeis CATEGORICAL
        },
      ],
      "startTime": "A String", # Output only. Time at which the trial was started.
      "state": "A String", # The detailed state of a trial.
      "trialInfeasible": True or False, # Output only. If true, the parameters in this trial are not attempted again.
    }

`suggest(parent, body=None, x__xgafv=None)`

    Adds one or more trials to a study, with parameter values suggested by AI Platform Vizier. Returns a long-running operation associated with the generation of trial suggestions. When this long-running operation succeeds, it will contain a SuggestTrialsResponse.

    Args:
      parent: string, Required. The name of the study that the trial belongs to. (required)
      body: object, The request body.
        The object takes the form of:

    { # The request message for the SuggestTrial service method.
      "clientId": "A String", # Required. The identifier of the client that is requesting the suggestion. If multiple SuggestTrialsRequests have the same `client_id`, the service will return the identical suggested trial if the trial is pending, and provide a new trial if the last suggested trial was completed.
      "suggestionCount": 42, # Required. The number of suggestions requested.
    }

      x__xgafv: string, V1 error format.
        Allowed values
          1 - v1 error format
          2 - v2 error format

    Returns:
      An object of the form:

        { # This resource represents a long-running operation that is the result of a network API call.
      "done": True or False, # 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.
      "error": { # 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). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
        "code": 42, # The status code, which should be an enum value of google.rpc.Code.
        "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
          {
            "a_key": "", # Properties of the object. Contains field @type with type URL.
          },
        ],
        "message": "A String", # 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.
      },
      "metadata": { # 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.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
      "name": "A String", # 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 be a resource name ending with `operations/{unique_id}`.
      "response": { # The normal, successful response of the operation. 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`.
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    }