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<h1><a href="ml_v1.html">Cloud Machine Learning Engine</a> . <a href="ml_v1.projects.html">projects</a> . <a href="ml_v1.projects.models.html">models</a></h1>
<h2>Instance Methods</h2>
<p class="toc_element">
  <code><a href="ml_v1.projects.models.versions.html">versions()</a></code>
</p>
<p class="firstline">Returns the versions Resource.</p>

<p class="toc_element">
  <code><a href="#create">create(parent, body, x__xgafv=None)</a></code></p>
<p class="firstline">Creates a model which will later contain one or more versions.</p>
<p class="toc_element">
  <code><a href="#delete">delete(name, x__xgafv=None)</a></code></p>
<p class="firstline">Deletes a model.</p>
<p class="toc_element">
  <code><a href="#get">get(name, x__xgafv=None)</a></code></p>
<p class="firstline">Gets information about a model, including its name, the description (if</p>
<p class="toc_element">
  <code><a href="#getIamPolicy">getIamPolicy(resource, x__xgafv=None)</a></code></p>
<p class="firstline">Gets the access control policy for a resource.</p>
<p class="toc_element">
  <code><a href="#list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</a></code></p>
<p class="firstline">Lists the models in a project.</p>
<p class="toc_element">
  <code><a href="#list_next">list_next(previous_request, previous_response)</a></code></p>
<p class="firstline">Retrieves the next page of results.</p>
<p class="toc_element">
  <code><a href="#patch">patch(name, body, updateMask=None, x__xgafv=None)</a></code></p>
<p class="firstline">Updates a specific model resource.</p>
<p class="toc_element">
  <code><a href="#setIamPolicy">setIamPolicy(resource, body, x__xgafv=None)</a></code></p>
<p class="firstline">Sets the access control policy on the specified resource. Replaces any</p>
<p class="toc_element">
  <code><a href="#testIamPermissions">testIamPermissions(resource, body, x__xgafv=None)</a></code></p>
<p class="firstline">Returns permissions that a caller has on the specified resource.</p>
<h3>Method Details</h3>
<div class="method">
    <code class="details" id="create">create(parent, body, x__xgafv=None)</code>
  <pre>Creates a model which will later contain one or more versions.

You must add at least one version before you can request predictions from
the model. Add versions by calling
[projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create).

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

{ # 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.
  "description": "A String", # Optional. The description specified for the model when it was created.
  "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout`
      # streams to Stackdriver Logging. These can be more verbose than the standard
      # access logs (see `onlinePredictionLogging`) and can incur higher cost.
      # However, they are helpful for debugging. Note that
      # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
      # your project receives prediction requests at a high QPS. Estimate your
      # costs before enabling this option.
      # 
      # Default is false.
  "labels": { # Optional. One or more labels that you can add, to organize your models.
      # Each label is a key-value pair, where both the key and the value are
      # arbitrary strings that you supply.
      # For more information, see the documentation on
      # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
    "a_key": "A String",
  },
  "regions": [ # 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.
      # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
      # for AI Platform services.
      # 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.
    "A String",
  ],
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
      # prevent simultaneous updates of a model from overwriting each other.
      # It is strongly suggested that systems make use of the `etag` in the
      # read-modify-write cycle to perform model updates in order to avoid race
      # conditions: An `etag` is returned in the response to `GetModel`, and
      # systems are expected to put that etag in the request to `UpdateModel` to
      # ensure that their change will be applied to the model as intended.
  "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
      #
      # 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).
    "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
    "labels": { # Optional. One or more labels that you can add, to organize your model
        # versions. Each label is a key-value pair, where both the key and the value
        # are arbitrary strings that you supply.
        # For more information, see the documentation on
        # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
      "a_key": "A String",
    },
    "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
        # applies to online prediction service.
        # <dl>
        #   <dt>mls1-c1-m2</dt>
        #   <dd>
        #   The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
        #   name for this machine type is "mls1-highmem-1".
        #   </dd>
        #   <dt>mls1-c4-m2</dt>
        #   <dd>
        #   In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
        #   deprecated name for this machine type is "mls1-highcpu-4".
        #   </dd>
        # </dl>
    "description": "A String", # Optional. The description specified for the version when it was created.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
        # If not set, AI Platform uses the default stable version, 1.0. For more
        # information, see the
        # [runtime version list](/ml-engine/docs/runtime-version-list) and
        # [how to manage runtime versions](/ml-engine/docs/versioning).
    "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
        # model. You should generally use `auto_scaling` with an appropriate
        # `min_nodes` instead, but this option is available if you want more
        # predictable billing. Beware that latency and error rates will increase
        # if the traffic exceeds that capability of the system to serve it based
        # on the selected number of nodes.
      "nodes": 42, # 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
          # last billing cycle plus the cost for each prediction performed.
    },
    "predictionClass": "A String", # Optional. The fully qualified name
        # (<var>module_name</var>.<var>class_name</var>) of a class that implements
        # the Predictor interface described in this reference field. The module
        # containing this class should be included in a package provided to the
        # [`packageUris` field](#Version.FIELDS.package_uris).
        #
        # Specify this field if and only if you are deploying a [custom prediction
        # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
        # If you specify this field, you must set
        # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
        #
        # The following code sample provides the Predictor interface:
        #
        # ```py
        # class Predictor(object):
        # """Interface for constructing custom predictors."""
        #
        # def predict(self, instances, **kwargs):
        #     """Performs custom prediction.
        #
        #     Instances are the decoded values from the request. They have already
        #     been deserialized from JSON.
        #
        #     Args:
        #         instances: A list of prediction input instances.
        #         **kwargs: A dictionary of keyword args provided as additional
        #             fields on the predict request body.
        #
        #     Returns:
        #         A list of outputs containing the prediction results. This list must
        #         be JSON serializable.
        #     """
        #     raise NotImplementedError()
        #
        # @classmethod
        # def from_path(cls, model_dir):
        #     """Creates an instance of Predictor using the given path.
        #
        #     Loading of the predictor should be done in this method.
        #
        #     Args:
        #         model_dir: The local directory that contains the exported model
        #             file along with any additional files uploaded when creating the
        #             version resource.
        #
        #     Returns:
        #         An instance implementing this Predictor class.
        #     """
        #     raise NotImplementedError()
        # ```
        #
        # Learn more about [the Predictor interface and custom prediction
        # routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
    "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
        # response to increases and decreases in traffic. Care should be
        # taken to ramp up traffic according to the model's ability to scale
        # or you will start seeing increases in latency and 429 response codes.
      "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
          # nodes are always up, starting from the time the model is deployed.
          # Therefore, the cost of operating this model will be at least
          # `rate` * `min_nodes` * number of hours since last billing cycle,
          # where `rate` is the cost per node-hour as documented in the
          # [pricing guide](/ml-engine/docs/pricing),
          # even if no predictions are performed. There is additional cost for each
          # prediction performed.
          #
          # Unlike manual scaling, if the load gets too heavy for the nodes
          # that are up, the service will automatically add nodes to handle the
          # increased load as well as scale back as traffic drops, always maintaining
          # at least `min_nodes`. You will be charged for the time in which additional
          # nodes are used.
          #
          # If not specified, `min_nodes` defaults to 0, in which case, when traffic
          # to a model stops (and after a cool-down period), nodes will be shut down
          # and no charges will be incurred until traffic to the model resumes.
          #
          # You can set `min_nodes` when creating the model version, and you can also
          # update `min_nodes` for an existing version:
          # <pre>
          # update_body.json:
          # {
          #   'autoScaling': {
          #     'minNodes': 5
          #   }
          # }
          # </pre>
          # HTTP request:
          # <pre>
          # PATCH
          # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
          # -d @./update_body.json
          # </pre>
    },
    "serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
    "state": "A String", # Output only. The state of a version.
    "pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
        # version is '2.7'. Python '3.5' is available when `runtime_version` is set
        # to '1.4' and above. Python '2.7' works with all supported runtime versions.
    "framework": "A String", # Optional. The machine learning framework AI Platform uses to train
        # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`,
        # `XGBOOST`. If you do not specify a framework, AI Platform
        # will analyze files in the deployment_uri to determine a framework. If you
        # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version
        # of the model to 1.4 or greater.
        #
        # Do **not** specify a framework if you're deploying a [custom
        # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
    "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
        # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
        # or [scikit-learn pipelines with custom
        # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
        #
        # For a custom prediction routine, one of these packages must contain your
        # Predictor class (see
        # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
        # include any dependencies used by your Predictor or scikit-learn pipeline
        # uses that are not already included in your selected [runtime
        # version](/ml-engine/docs/tensorflow/runtime-version-list).
        #
        # If you specify this field, you must also set
        # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
      "A String",
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a model from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform model updates in order to avoid race
        # conditions: An `etag` is returned in the response to `GetVersion`, and
        # systems are expected to put that etag in the request to `UpdateVersion` to
        # ensure that their change will be applied to the model as intended.
    "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
    "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
        # create the version. See the
        # [guide to model
        # deployment](/ml-engine/docs/tensorflow/deploying-models) for more
        # information.
        #
        # 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.
        # The total number of model files can't exceed 1000.
    "createTime": "A String", # Output only. The time the version was created.
    "isDefault": True or False, # 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).
    "name": "A String", # Required.The name specified for the version when it was created.
        #
        # The version name must be unique within the model it is created in.
  },
  "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver
      # Logging. These logs are like standard server access logs, containing
      # information like timestamp and latency for each request. Note that
      # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
      # your project receives prediction requests at a high queries per second rate
      # (QPS). Estimate your costs before enabling this option.
      # 
      # Default is false.
  "name": "A String", # Required. The name specified for the model when it was created.
      # 
      # The model name must be unique within the project it is created in.
}

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

Returns:
  An object of the form:

    { # 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.
    "description": "A String", # Optional. The description specified for the model when it was created.
    "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout`
        # streams to Stackdriver Logging. These can be more verbose than the standard
        # access logs (see `onlinePredictionLogging`) and can incur higher cost.
        # However, they are helpful for debugging. Note that
        # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
        # your project receives prediction requests at a high QPS. Estimate your
        # costs before enabling this option.
        #
        # Default is false.
    "labels": { # Optional. One or more labels that you can add, to organize your models.
        # Each label is a key-value pair, where both the key and the value are
        # arbitrary strings that you supply.
        # For more information, see the documentation on
        # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
      "a_key": "A String",
    },
    "regions": [ # 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.
        # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
        # for AI Platform services.
        # 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.
      "A String",
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a model from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform model updates in order to avoid race
        # conditions: An `etag` is returned in the response to `GetModel`, and
        # systems are expected to put that etag in the request to `UpdateModel` to
        # ensure that their change will be applied to the model as intended.
    "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
        #
        # 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).
      "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
      "labels": { # Optional. One or more labels that you can add, to organize your model
          # versions. Each label is a key-value pair, where both the key and the value
          # are arbitrary strings that you supply.
          # For more information, see the documentation on
          # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
        "a_key": "A String",
      },
      "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
          # applies to online prediction service.
          # <dl>
          #   <dt>mls1-c1-m2</dt>
          #   <dd>
          #   The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
          #   name for this machine type is "mls1-highmem-1".
          #   </dd>
          #   <dt>mls1-c4-m2</dt>
          #   <dd>
          #   In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
          #   deprecated name for this machine type is "mls1-highcpu-4".
          #   </dd>
          # </dl>
      "description": "A String", # Optional. The description specified for the version when it was created.
      "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
          # If not set, AI Platform uses the default stable version, 1.0. For more
          # information, see the
          # [runtime version list](/ml-engine/docs/runtime-version-list) and
          # [how to manage runtime versions](/ml-engine/docs/versioning).
      "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
          # model. You should generally use `auto_scaling` with an appropriate
          # `min_nodes` instead, but this option is available if you want more
          # predictable billing. Beware that latency and error rates will increase
          # if the traffic exceeds that capability of the system to serve it based
          # on the selected number of nodes.
        "nodes": 42, # 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
            # last billing cycle plus the cost for each prediction performed.
      },
      "predictionClass": "A String", # Optional. The fully qualified name
          # (<var>module_name</var>.<var>class_name</var>) of a class that implements
          # the Predictor interface described in this reference field. The module
          # containing this class should be included in a package provided to the
          # [`packageUris` field](#Version.FIELDS.package_uris).
          #
          # Specify this field if and only if you are deploying a [custom prediction
          # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
          # If you specify this field, you must set
          # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
          #
          # The following code sample provides the Predictor interface:
          #
          # ```py
          # class Predictor(object):
          # """Interface for constructing custom predictors."""
          #
          # def predict(self, instances, **kwargs):
          #     """Performs custom prediction.
          #
          #     Instances are the decoded values from the request. They have already
          #     been deserialized from JSON.
          #
          #     Args:
          #         instances: A list of prediction input instances.
          #         **kwargs: A dictionary of keyword args provided as additional
          #             fields on the predict request body.
          #
          #     Returns:
          #         A list of outputs containing the prediction results. This list must
          #         be JSON serializable.
          #     """
          #     raise NotImplementedError()
          #
          # @classmethod
          # def from_path(cls, model_dir):
          #     """Creates an instance of Predictor using the given path.
          #
          #     Loading of the predictor should be done in this method.
          #
          #     Args:
          #         model_dir: The local directory that contains the exported model
          #             file along with any additional files uploaded when creating the
          #             version resource.
          #
          #     Returns:
          #         An instance implementing this Predictor class.
          #     """
          #     raise NotImplementedError()
          # ```
          #
          # Learn more about [the Predictor interface and custom prediction
          # routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
      "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
          # response to increases and decreases in traffic. Care should be
          # taken to ramp up traffic according to the model's ability to scale
          # or you will start seeing increases in latency and 429 response codes.
        "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
            # nodes are always up, starting from the time the model is deployed.
            # Therefore, the cost of operating this model will be at least
            # `rate` * `min_nodes` * number of hours since last billing cycle,
            # where `rate` is the cost per node-hour as documented in the
            # [pricing guide](/ml-engine/docs/pricing),
            # even if no predictions are performed. There is additional cost for each
            # prediction performed.
            #
            # Unlike manual scaling, if the load gets too heavy for the nodes
            # that are up, the service will automatically add nodes to handle the
            # increased load as well as scale back as traffic drops, always maintaining
            # at least `min_nodes`. You will be charged for the time in which additional
            # nodes are used.
            #
            # If not specified, `min_nodes` defaults to 0, in which case, when traffic
            # to a model stops (and after a cool-down period), nodes will be shut down
            # and no charges will be incurred until traffic to the model resumes.
            #
            # You can set `min_nodes` when creating the model version, and you can also
            # update `min_nodes` for an existing version:
            # <pre>
            # update_body.json:
            # {
            #   'autoScaling': {
            #     'minNodes': 5
            #   }
            # }
            # </pre>
            # HTTP request:
            # <pre>
            # PATCH
            # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
            # -d @./update_body.json
            # </pre>
      },
      "serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
      "state": "A String", # Output only. The state of a version.
      "pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
          # version is '2.7'. Python '3.5' is available when `runtime_version` is set
          # to '1.4' and above. Python '2.7' works with all supported runtime versions.
      "framework": "A String", # Optional. The machine learning framework AI Platform uses to train
          # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`,
          # `XGBOOST`. If you do not specify a framework, AI Platform
          # will analyze files in the deployment_uri to determine a framework. If you
          # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version
          # of the model to 1.4 or greater.
          #
          # Do **not** specify a framework if you're deploying a [custom
          # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
      "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
          # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
          # or [scikit-learn pipelines with custom
          # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
          #
          # For a custom prediction routine, one of these packages must contain your
          # Predictor class (see
          # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
          # include any dependencies used by your Predictor or scikit-learn pipeline
          # uses that are not already included in your selected [runtime
          # version](/ml-engine/docs/tensorflow/runtime-version-list).
          #
          # If you specify this field, you must also set
          # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
        "A String",
      ],
      "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
          # prevent simultaneous updates of a model from overwriting each other.
          # It is strongly suggested that systems make use of the `etag` in the
          # read-modify-write cycle to perform model updates in order to avoid race
          # conditions: An `etag` is returned in the response to `GetVersion`, and
          # systems are expected to put that etag in the request to `UpdateVersion` to
          # ensure that their change will be applied to the model as intended.
      "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
      "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
          # create the version. See the
          # [guide to model
          # deployment](/ml-engine/docs/tensorflow/deploying-models) for more
          # information.
          #
          # 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.
          # The total number of model files can't exceed 1000.
      "createTime": "A String", # Output only. The time the version was created.
      "isDefault": True or False, # 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).
      "name": "A String", # Required.The name specified for the version when it was created.
          #
          # The version name must be unique within the model it is created in.
    },
    "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver
        # Logging. These logs are like standard server access logs, containing
        # information like timestamp and latency for each request. Note that
        # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
        # your project receives prediction requests at a high queries per second rate
        # (QPS). Estimate your costs before enabling this option.
        #
        # Default is false.
    "name": "A String", # Required. The name specified for the model when it was created.
        #
        # The model name must be unique within the project it is created in.
  }</pre>
</div>

<div class="method">
    <code class="details" id="delete">delete(name, x__xgafv=None)</code>
  <pre>Deletes a model.

You can only delete a model if there are no versions in it. You can delete
versions by calling
[projects.models.versions.delete](/ml-engine/reference/rest/v1/projects.models.versions/delete).

Args:
  name: string, Required. The name of the model. (required)
  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.
    "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.
    },
    "error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
        # 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).
      "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.
      "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.
        },
      ],
    },
    "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.
    "response": { # 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`.
      "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}`.
  }</pre>
</div>

<div class="method">
    <code class="details" id="get">get(name, x__xgafv=None)</code>
  <pre>Gets information about a model, including its name, the description (if
set), and the default version (if at least one version of the model has
been deployed).

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

Returns:
  An object of the form:

    { # 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.
    "description": "A String", # Optional. The description specified for the model when it was created.
    "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout`
        # streams to Stackdriver Logging. These can be more verbose than the standard
        # access logs (see `onlinePredictionLogging`) and can incur higher cost.
        # However, they are helpful for debugging. Note that
        # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
        # your project receives prediction requests at a high QPS. Estimate your
        # costs before enabling this option.
        #
        # Default is false.
    "labels": { # Optional. One or more labels that you can add, to organize your models.
        # Each label is a key-value pair, where both the key and the value are
        # arbitrary strings that you supply.
        # For more information, see the documentation on
        # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
      "a_key": "A String",
    },
    "regions": [ # 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.
        # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
        # for AI Platform services.
        # 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.
      "A String",
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a model from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform model updates in order to avoid race
        # conditions: An `etag` is returned in the response to `GetModel`, and
        # systems are expected to put that etag in the request to `UpdateModel` to
        # ensure that their change will be applied to the model as intended.
    "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
        #
        # 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).
      "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
      "labels": { # Optional. One or more labels that you can add, to organize your model
          # versions. Each label is a key-value pair, where both the key and the value
          # are arbitrary strings that you supply.
          # For more information, see the documentation on
          # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
        "a_key": "A String",
      },
      "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
          # applies to online prediction service.
          # <dl>
          #   <dt>mls1-c1-m2</dt>
          #   <dd>
          #   The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
          #   name for this machine type is "mls1-highmem-1".
          #   </dd>
          #   <dt>mls1-c4-m2</dt>
          #   <dd>
          #   In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
          #   deprecated name for this machine type is "mls1-highcpu-4".
          #   </dd>
          # </dl>
      "description": "A String", # Optional. The description specified for the version when it was created.
      "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
          # If not set, AI Platform uses the default stable version, 1.0. For more
          # information, see the
          # [runtime version list](/ml-engine/docs/runtime-version-list) and
          # [how to manage runtime versions](/ml-engine/docs/versioning).
      "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
          # model. You should generally use `auto_scaling` with an appropriate
          # `min_nodes` instead, but this option is available if you want more
          # predictable billing. Beware that latency and error rates will increase
          # if the traffic exceeds that capability of the system to serve it based
          # on the selected number of nodes.
        "nodes": 42, # 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
            # last billing cycle plus the cost for each prediction performed.
      },
      "predictionClass": "A String", # Optional. The fully qualified name
          # (<var>module_name</var>.<var>class_name</var>) of a class that implements
          # the Predictor interface described in this reference field. The module
          # containing this class should be included in a package provided to the
          # [`packageUris` field](#Version.FIELDS.package_uris).
          #
          # Specify this field if and only if you are deploying a [custom prediction
          # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
          # If you specify this field, you must set
          # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
          #
          # The following code sample provides the Predictor interface:
          #
          # ```py
          # class Predictor(object):
          # """Interface for constructing custom predictors."""
          #
          # def predict(self, instances, **kwargs):
          #     """Performs custom prediction.
          #
          #     Instances are the decoded values from the request. They have already
          #     been deserialized from JSON.
          #
          #     Args:
          #         instances: A list of prediction input instances.
          #         **kwargs: A dictionary of keyword args provided as additional
          #             fields on the predict request body.
          #
          #     Returns:
          #         A list of outputs containing the prediction results. This list must
          #         be JSON serializable.
          #     """
          #     raise NotImplementedError()
          #
          # @classmethod
          # def from_path(cls, model_dir):
          #     """Creates an instance of Predictor using the given path.
          #
          #     Loading of the predictor should be done in this method.
          #
          #     Args:
          #         model_dir: The local directory that contains the exported model
          #             file along with any additional files uploaded when creating the
          #             version resource.
          #
          #     Returns:
          #         An instance implementing this Predictor class.
          #     """
          #     raise NotImplementedError()
          # ```
          #
          # Learn more about [the Predictor interface and custom prediction
          # routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
      "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
          # response to increases and decreases in traffic. Care should be
          # taken to ramp up traffic according to the model's ability to scale
          # or you will start seeing increases in latency and 429 response codes.
        "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
            # nodes are always up, starting from the time the model is deployed.
            # Therefore, the cost of operating this model will be at least
            # `rate` * `min_nodes` * number of hours since last billing cycle,
            # where `rate` is the cost per node-hour as documented in the
            # [pricing guide](/ml-engine/docs/pricing),
            # even if no predictions are performed. There is additional cost for each
            # prediction performed.
            #
            # Unlike manual scaling, if the load gets too heavy for the nodes
            # that are up, the service will automatically add nodes to handle the
            # increased load as well as scale back as traffic drops, always maintaining
            # at least `min_nodes`. You will be charged for the time in which additional
            # nodes are used.
            #
            # If not specified, `min_nodes` defaults to 0, in which case, when traffic
            # to a model stops (and after a cool-down period), nodes will be shut down
            # and no charges will be incurred until traffic to the model resumes.
            #
            # You can set `min_nodes` when creating the model version, and you can also
            # update `min_nodes` for an existing version:
            # <pre>
            # update_body.json:
            # {
            #   'autoScaling': {
            #     'minNodes': 5
            #   }
            # }
            # </pre>
            # HTTP request:
            # <pre>
            # PATCH
            # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
            # -d @./update_body.json
            # </pre>
      },
      "serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
      "state": "A String", # Output only. The state of a version.
      "pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
          # version is '2.7'. Python '3.5' is available when `runtime_version` is set
          # to '1.4' and above. Python '2.7' works with all supported runtime versions.
      "framework": "A String", # Optional. The machine learning framework AI Platform uses to train
          # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`,
          # `XGBOOST`. If you do not specify a framework, AI Platform
          # will analyze files in the deployment_uri to determine a framework. If you
          # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version
          # of the model to 1.4 or greater.
          #
          # Do **not** specify a framework if you're deploying a [custom
          # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
      "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
          # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
          # or [scikit-learn pipelines with custom
          # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
          #
          # For a custom prediction routine, one of these packages must contain your
          # Predictor class (see
          # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
          # include any dependencies used by your Predictor or scikit-learn pipeline
          # uses that are not already included in your selected [runtime
          # version](/ml-engine/docs/tensorflow/runtime-version-list).
          #
          # If you specify this field, you must also set
          # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
        "A String",
      ],
      "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
          # prevent simultaneous updates of a model from overwriting each other.
          # It is strongly suggested that systems make use of the `etag` in the
          # read-modify-write cycle to perform model updates in order to avoid race
          # conditions: An `etag` is returned in the response to `GetVersion`, and
          # systems are expected to put that etag in the request to `UpdateVersion` to
          # ensure that their change will be applied to the model as intended.
      "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
      "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
          # create the version. See the
          # [guide to model
          # deployment](/ml-engine/docs/tensorflow/deploying-models) for more
          # information.
          #
          # 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.
          # The total number of model files can't exceed 1000.
      "createTime": "A String", # Output only. The time the version was created.
      "isDefault": True or False, # 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).
      "name": "A String", # Required.The name specified for the version when it was created.
          #
          # The version name must be unique within the model it is created in.
    },
    "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver
        # Logging. These logs are like standard server access logs, containing
        # information like timestamp and latency for each request. Note that
        # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
        # your project receives prediction requests at a high queries per second rate
        # (QPS). Estimate your costs before enabling this option.
        #
        # Default is false.
    "name": "A String", # Required. The name specified for the model when it was created.
        #
        # The model name must be unique within the project it is created in.
  }</pre>
</div>

<div class="method">
    <code class="details" id="getIamPolicy">getIamPolicy(resource, x__xgafv=None)</code>
  <pre>Gets the access control policy for a resource.
Returns an empty policy if the resource exists and does not have a policy
set.

Args:
  resource: string, REQUIRED: The resource for which the policy is being requested.
See the operation documentation for the appropriate value for this field. (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Defines an Identity and Access Management (IAM) policy. It is used to
      # specify access control policies for Cloud Platform resources.
      #
      #
      # A `Policy` consists of a list of `bindings`. A `binding` binds a list of
      # `members` to a `role`, where the members can be user accounts, Google groups,
      # Google domains, and service accounts. A `role` is a named list of permissions
      # defined by IAM.
      #
      # **JSON Example**
      #
      #     {
      #       "bindings": [
      #         {
      #           "role": "roles/owner",
      #           "members": [
      #             "user:mike@example.com",
      #             "group:admins@example.com",
      #             "domain:google.com",
      #             "serviceAccount:my-other-app@appspot.gserviceaccount.com"
      #           ]
      #         },
      #         {
      #           "role": "roles/viewer",
      #           "members": ["user:sean@example.com"]
      #         }
      #       ]
      #     }
      #
      # **YAML Example**
      #
      #     bindings:
      #     - members:
      #       - user:mike@example.com
      #       - group:admins@example.com
      #       - domain:google.com
      #       - serviceAccount:my-other-app@appspot.gserviceaccount.com
      #       role: roles/owner
      #     - members:
      #       - user:sean@example.com
      #       role: roles/viewer
      #
      #
      # For a description of IAM and its features, see the
      # [IAM developer's guide](https://cloud.google.com/iam/docs).
    "bindings": [ # Associates a list of `members` to a `role`.
        # `bindings` with no members will result in an error.
      { # Associates `members` with a `role`.
        "role": "A String", # Role that is assigned to `members`.
            # For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
        "members": [ # Specifies the identities requesting access for a Cloud Platform resource.
            # `members` can have the following values:
            #
            # * `allUsers`: A special identifier that represents anyone who is
            #    on the internet; with or without a Google account.
            #
            # * `allAuthenticatedUsers`: A special identifier that represents anyone
            #    who is authenticated with a Google account or a service account.
            #
            # * `user:{emailid}`: An email address that represents a specific Google
            #    account. For example, `alice@gmail.com` .
            #
            #
            # * `serviceAccount:{emailid}`: An email address that represents a service
            #    account. For example, `my-other-app@appspot.gserviceaccount.com`.
            #
            # * `group:{emailid}`: An email address that represents a Google group.
            #    For example, `admins@example.com`.
            #
            #
            # * `domain:{domain}`: The G Suite domain (primary) that represents all the
            #    users of that domain. For example, `google.com` or `example.com`.
            #
          "A String",
        ],
        "condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
            # NOTE: An unsatisfied condition will not allow user access via current
            # binding. Different bindings, including their conditions, are examined
            # independently.
            #
            #     title: "User account presence"
            #     description: "Determines whether the request has a user account"
            #     expression: "size(request.user) > 0"
          "description": "A String", # An optional description of the expression. This is a longer text which
              # describes the expression, e.g. when hovered over it in a UI.
          "expression": "A String", # Textual representation of an expression in
              # Common Expression Language syntax.
              #
              # The application context of the containing message determines which
              # well-known feature set of CEL is supported.
          "location": "A String", # An optional string indicating the location of the expression for error
              # reporting, e.g. a file name and a position in the file.
          "title": "A String", # An optional title for the expression, i.e. a short string describing
              # its purpose. This can be used e.g. in UIs which allow to enter the
              # expression.
        },
      },
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a policy from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform policy updates in order to avoid race
        # conditions: An `etag` is returned in the response to `getIamPolicy`, and
        # systems are expected to put that etag in the request to `setIamPolicy` to
        # ensure that their change will be applied to the same version of the policy.
        #
        # If no `etag` is provided in the call to `setIamPolicy`, then the existing
        # policy is overwritten blindly.
    "version": 42, # Deprecated.
    "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
      { # Specifies the audit configuration for a service.
          # The configuration determines which permission types are logged, and what
          # identities, if any, are exempted from logging.
          # An AuditConfig must have one or more AuditLogConfigs.
          #
          # If there are AuditConfigs for both `allServices` and a specific service,
          # the union of the two AuditConfigs is used for that service: the log_types
          # specified in each AuditConfig are enabled, and the exempted_members in each
          # AuditLogConfig are exempted.
          #
          # Example Policy with multiple AuditConfigs:
          #
          #     {
          #       "audit_configs": [
          #         {
          #           "service": "allServices"
          #           "audit_log_configs": [
          #             {
          #               "log_type": "DATA_READ",
          #               "exempted_members": [
          #                 "user:foo@gmail.com"
          #               ]
          #             },
          #             {
          #               "log_type": "DATA_WRITE",
          #             },
          #             {
          #               "log_type": "ADMIN_READ",
          #             }
          #           ]
          #         },
          #         {
          #           "service": "fooservice.googleapis.com"
          #           "audit_log_configs": [
          #             {
          #               "log_type": "DATA_READ",
          #             },
          #             {
          #               "log_type": "DATA_WRITE",
          #               "exempted_members": [
          #                 "user:bar@gmail.com"
          #               ]
          #             }
          #           ]
          #         }
          #       ]
          #     }
          #
          # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
          # logging. It also exempts foo@gmail.com from DATA_READ logging, and
          # bar@gmail.com from DATA_WRITE logging.
        "auditLogConfigs": [ # The configuration for logging of each type of permission.
          { # Provides the configuration for logging a type of permissions.
              # Example:
              #
              #     {
              #       "audit_log_configs": [
              #         {
              #           "log_type": "DATA_READ",
              #           "exempted_members": [
              #             "user:foo@gmail.com"
              #           ]
              #         },
              #         {
              #           "log_type": "DATA_WRITE",
              #         }
              #       ]
              #     }
              #
              # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
              # foo@gmail.com from DATA_READ logging.
            "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
                # permission.
                # Follows the same format of Binding.members.
              "A String",
            ],
            "logType": "A String", # The log type that this config enables.
          },
        ],
        "service": "A String", # Specifies a service that will be enabled for audit logging.
            # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
            # `allServices` is a special value that covers all services.
      },
    ],
  }</pre>
</div>

<div class="method">
    <code class="details" id="list">list(parent, pageToken=None, x__xgafv=None, pageSize=None, filter=None)</code>
  <pre>Lists the models in a project.

Each project can contain multiple models, and each model can have multiple
versions.

If there are no models that match the request parameters, the list request
returns an empty response body: {}.

Args:
  parent: string, Required. The name of the project whose models are to be listed. (required)
  pageToken: string, Optional. A page token to request the next page of results.

You get the token from the `next_page_token` field of the response from
the previous call.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format
  pageSize: integer, Optional. The number of models to retrieve per "page" of results. If there
are more remaining results than this number, the response message will
contain a valid value in the `next_page_token` field.

The default value is 20, and the maximum page size is 100.
  filter: string, Optional. Specifies the subset of models to retrieve.

Returns:
  An object of the form:

    { # Response message for the ListModels method.
    "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
        # subsequent call.
    "models": [ # The list of models.
      { # 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.
        "description": "A String", # Optional. The description specified for the model when it was created.
        "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout`
            # streams to Stackdriver Logging. These can be more verbose than the standard
            # access logs (see `onlinePredictionLogging`) and can incur higher cost.
            # However, they are helpful for debugging. Note that
            # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
            # your project receives prediction requests at a high QPS. Estimate your
            # costs before enabling this option.
            #
            # Default is false.
        "labels": { # Optional. One or more labels that you can add, to organize your models.
            # Each label is a key-value pair, where both the key and the value are
            # arbitrary strings that you supply.
            # For more information, see the documentation on
            # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
          "a_key": "A String",
        },
        "regions": [ # 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.
            # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
            # for AI Platform services.
            # 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.
          "A String",
        ],
        "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
            # prevent simultaneous updates of a model from overwriting each other.
            # It is strongly suggested that systems make use of the `etag` in the
            # read-modify-write cycle to perform model updates in order to avoid race
            # conditions: An `etag` is returned in the response to `GetModel`, and
            # systems are expected to put that etag in the request to `UpdateModel` to
            # ensure that their change will be applied to the model as intended.
        "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
            #
            # 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).
          "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
          "labels": { # Optional. One or more labels that you can add, to organize your model
              # versions. Each label is a key-value pair, where both the key and the value
              # are arbitrary strings that you supply.
              # For more information, see the documentation on
              # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
            "a_key": "A String",
          },
          "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
              # applies to online prediction service.
              # <dl>
              #   <dt>mls1-c1-m2</dt>
              #   <dd>
              #   The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
              #   name for this machine type is "mls1-highmem-1".
              #   </dd>
              #   <dt>mls1-c4-m2</dt>
              #   <dd>
              #   In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
              #   deprecated name for this machine type is "mls1-highcpu-4".
              #   </dd>
              # </dl>
          "description": "A String", # Optional. The description specified for the version when it was created.
          "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
              # If not set, AI Platform uses the default stable version, 1.0. For more
              # information, see the
              # [runtime version list](/ml-engine/docs/runtime-version-list) and
              # [how to manage runtime versions](/ml-engine/docs/versioning).
          "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
              # model. You should generally use `auto_scaling` with an appropriate
              # `min_nodes` instead, but this option is available if you want more
              # predictable billing. Beware that latency and error rates will increase
              # if the traffic exceeds that capability of the system to serve it based
              # on the selected number of nodes.
            "nodes": 42, # 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
                # last billing cycle plus the cost for each prediction performed.
          },
          "predictionClass": "A String", # Optional. The fully qualified name
              # (<var>module_name</var>.<var>class_name</var>) of a class that implements
              # the Predictor interface described in this reference field. The module
              # containing this class should be included in a package provided to the
              # [`packageUris` field](#Version.FIELDS.package_uris).
              #
              # Specify this field if and only if you are deploying a [custom prediction
              # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
              # If you specify this field, you must set
              # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
              #
              # The following code sample provides the Predictor interface:
              #
              # ```py
              # class Predictor(object):
              # """Interface for constructing custom predictors."""
              #
              # def predict(self, instances, **kwargs):
              #     """Performs custom prediction.
              #
              #     Instances are the decoded values from the request. They have already
              #     been deserialized from JSON.
              #
              #     Args:
              #         instances: A list of prediction input instances.
              #         **kwargs: A dictionary of keyword args provided as additional
              #             fields on the predict request body.
              #
              #     Returns:
              #         A list of outputs containing the prediction results. This list must
              #         be JSON serializable.
              #     """
              #     raise NotImplementedError()
              #
              # @classmethod
              # def from_path(cls, model_dir):
              #     """Creates an instance of Predictor using the given path.
              #
              #     Loading of the predictor should be done in this method.
              #
              #     Args:
              #         model_dir: The local directory that contains the exported model
              #             file along with any additional files uploaded when creating the
              #             version resource.
              #
              #     Returns:
              #         An instance implementing this Predictor class.
              #     """
              #     raise NotImplementedError()
              # ```
              #
              # Learn more about [the Predictor interface and custom prediction
              # routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
          "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
              # response to increases and decreases in traffic. Care should be
              # taken to ramp up traffic according to the model's ability to scale
              # or you will start seeing increases in latency and 429 response codes.
            "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
                # nodes are always up, starting from the time the model is deployed.
                # Therefore, the cost of operating this model will be at least
                # `rate` * `min_nodes` * number of hours since last billing cycle,
                # where `rate` is the cost per node-hour as documented in the
                # [pricing guide](/ml-engine/docs/pricing),
                # even if no predictions are performed. There is additional cost for each
                # prediction performed.
                #
                # Unlike manual scaling, if the load gets too heavy for the nodes
                # that are up, the service will automatically add nodes to handle the
                # increased load as well as scale back as traffic drops, always maintaining
                # at least `min_nodes`. You will be charged for the time in which additional
                # nodes are used.
                #
                # If not specified, `min_nodes` defaults to 0, in which case, when traffic
                # to a model stops (and after a cool-down period), nodes will be shut down
                # and no charges will be incurred until traffic to the model resumes.
                #
                # You can set `min_nodes` when creating the model version, and you can also
                # update `min_nodes` for an existing version:
                # <pre>
                # update_body.json:
                # {
                #   'autoScaling': {
                #     'minNodes': 5
                #   }
                # }
                # </pre>
                # HTTP request:
                # <pre>
                # PATCH
                # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
                # -d @./update_body.json
                # </pre>
          },
          "serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
          "state": "A String", # Output only. The state of a version.
          "pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
              # version is '2.7'. Python '3.5' is available when `runtime_version` is set
              # to '1.4' and above. Python '2.7' works with all supported runtime versions.
          "framework": "A String", # Optional. The machine learning framework AI Platform uses to train
              # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`,
              # `XGBOOST`. If you do not specify a framework, AI Platform
              # will analyze files in the deployment_uri to determine a framework. If you
              # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version
              # of the model to 1.4 or greater.
              #
              # Do **not** specify a framework if you're deploying a [custom
              # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
          "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
              # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
              # or [scikit-learn pipelines with custom
              # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
              #
              # For a custom prediction routine, one of these packages must contain your
              # Predictor class (see
              # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
              # include any dependencies used by your Predictor or scikit-learn pipeline
              # uses that are not already included in your selected [runtime
              # version](/ml-engine/docs/tensorflow/runtime-version-list).
              #
              # If you specify this field, you must also set
              # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
            "A String",
          ],
          "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
              # prevent simultaneous updates of a model from overwriting each other.
              # It is strongly suggested that systems make use of the `etag` in the
              # read-modify-write cycle to perform model updates in order to avoid race
              # conditions: An `etag` is returned in the response to `GetVersion`, and
              # systems are expected to put that etag in the request to `UpdateVersion` to
              # ensure that their change will be applied to the model as intended.
          "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
          "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
              # create the version. See the
              # [guide to model
              # deployment](/ml-engine/docs/tensorflow/deploying-models) for more
              # information.
              #
              # 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.
              # The total number of model files can't exceed 1000.
          "createTime": "A String", # Output only. The time the version was created.
          "isDefault": True or False, # 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).
          "name": "A String", # Required.The name specified for the version when it was created.
              #
              # The version name must be unique within the model it is created in.
        },
        "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver
            # Logging. These logs are like standard server access logs, containing
            # information like timestamp and latency for each request. Note that
            # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
            # your project receives prediction requests at a high queries per second rate
            # (QPS). Estimate your costs before enabling this option.
            #
            # Default is false.
        "name": "A String", # Required. The name specified for the model when it was created.
            #
            # The model name must be unique within the project it is created in.
      },
    ],
  }</pre>
</div>

<div class="method">
    <code class="details" id="list_next">list_next(previous_request, previous_response)</code>
  <pre>Retrieves the next page of results.

Args:
  previous_request: The request for the previous page. (required)
  previous_response: The response from the request for the previous page. (required)

Returns:
  A request object that you can call 'execute()' on to request the next
  page. Returns None if there are no more items in the collection.
    </pre>
</div>

<div class="method">
    <code class="details" id="patch">patch(name, body, updateMask=None, x__xgafv=None)</code>
  <pre>Updates a specific model resource.

Currently the only supported fields to update are `description` and
`default_version.name`.

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

{ # 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.
  "description": "A String", # Optional. The description specified for the model when it was created.
  "onlinePredictionConsoleLogging": True or False, # Optional. If true, online prediction nodes send `stderr` and `stdout`
      # streams to Stackdriver Logging. These can be more verbose than the standard
      # access logs (see `onlinePredictionLogging`) and can incur higher cost.
      # However, they are helpful for debugging. Note that
      # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
      # your project receives prediction requests at a high QPS. Estimate your
      # costs before enabling this option.
      # 
      # Default is false.
  "labels": { # Optional. One or more labels that you can add, to organize your models.
      # Each label is a key-value pair, where both the key and the value are
      # arbitrary strings that you supply.
      # For more information, see the documentation on
      # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
    "a_key": "A String",
  },
  "regions": [ # 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.
      # See the <a href="/ml-engine/docs/tensorflow/regions">available regions</a>
      # for AI Platform services.
      # 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.
    "A String",
  ],
  "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
      # prevent simultaneous updates of a model from overwriting each other.
      # It is strongly suggested that systems make use of the `etag` in the
      # read-modify-write cycle to perform model updates in order to avoid race
      # conditions: An `etag` is returned in the response to `GetModel`, and
      # systems are expected to put that etag in the request to `UpdateModel` to
      # ensure that their change will be applied to the model as intended.
  "defaultVersion": { # Represents a version of the model. # Output only. The default version of the model. 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).
      #
      # 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).
    "errorMessage": "A String", # Output only. The details of a failure or a cancellation.
    "labels": { # Optional. One or more labels that you can add, to organize your model
        # versions. Each label is a key-value pair, where both the key and the value
        # are arbitrary strings that you supply.
        # For more information, see the documentation on
        # <a href="/ml-engine/docs/tensorflow/resource-labels">using labels</a>.
      "a_key": "A String",
    },
    "machineType": "A String", # Optional. The type of machine on which to serve the model. Currently only
        # applies to online prediction service.
        # <dl>
        #   <dt>mls1-c1-m2</dt>
        #   <dd>
        #   The <b>default</b> machine type, with 1 core and 2 GB RAM. The deprecated
        #   name for this machine type is "mls1-highmem-1".
        #   </dd>
        #   <dt>mls1-c4-m2</dt>
        #   <dd>
        #   In <b>Beta</b>. This machine type has 4 cores and 2 GB RAM. The
        #   deprecated name for this machine type is "mls1-highcpu-4".
        #   </dd>
        # </dl>
    "description": "A String", # Optional. The description specified for the version when it was created.
    "runtimeVersion": "A String", # Optional. The AI Platform runtime version to use for this deployment.
        # If not set, AI Platform uses the default stable version, 1.0. For more
        # information, see the
        # [runtime version list](/ml-engine/docs/runtime-version-list) and
        # [how to manage runtime versions](/ml-engine/docs/versioning).
    "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
        # model. You should generally use `auto_scaling` with an appropriate
        # `min_nodes` instead, but this option is available if you want more
        # predictable billing. Beware that latency and error rates will increase
        # if the traffic exceeds that capability of the system to serve it based
        # on the selected number of nodes.
      "nodes": 42, # 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
          # last billing cycle plus the cost for each prediction performed.
    },
    "predictionClass": "A String", # Optional. The fully qualified name
        # (<var>module_name</var>.<var>class_name</var>) of a class that implements
        # the Predictor interface described in this reference field. The module
        # containing this class should be included in a package provided to the
        # [`packageUris` field](#Version.FIELDS.package_uris).
        #
        # Specify this field if and only if you are deploying a [custom prediction
        # routine (beta)](/ml-engine/docs/tensorflow/custom-prediction-routines).
        # If you specify this field, you must set
        # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
        #
        # The following code sample provides the Predictor interface:
        #
        # ```py
        # class Predictor(object):
        # """Interface for constructing custom predictors."""
        #
        # def predict(self, instances, **kwargs):
        #     """Performs custom prediction.
        #
        #     Instances are the decoded values from the request. They have already
        #     been deserialized from JSON.
        #
        #     Args:
        #         instances: A list of prediction input instances.
        #         **kwargs: A dictionary of keyword args provided as additional
        #             fields on the predict request body.
        #
        #     Returns:
        #         A list of outputs containing the prediction results. This list must
        #         be JSON serializable.
        #     """
        #     raise NotImplementedError()
        #
        # @classmethod
        # def from_path(cls, model_dir):
        #     """Creates an instance of Predictor using the given path.
        #
        #     Loading of the predictor should be done in this method.
        #
        #     Args:
        #         model_dir: The local directory that contains the exported model
        #             file along with any additional files uploaded when creating the
        #             version resource.
        #
        #     Returns:
        #         An instance implementing this Predictor class.
        #     """
        #     raise NotImplementedError()
        # ```
        #
        # Learn more about [the Predictor interface and custom prediction
        # routines](/ml-engine/docs/tensorflow/custom-prediction-routines).
    "autoScaling": { # Options for automatically scaling a model. # Automatically scale the number of nodes used to serve the model in
        # response to increases and decreases in traffic. Care should be
        # taken to ramp up traffic according to the model's ability to scale
        # or you will start seeing increases in latency and 429 response codes.
      "minNodes": 42, # Optional. The minimum number of nodes to allocate for this model. These
          # nodes are always up, starting from the time the model is deployed.
          # Therefore, the cost of operating this model will be at least
          # `rate` * `min_nodes` * number of hours since last billing cycle,
          # where `rate` is the cost per node-hour as documented in the
          # [pricing guide](/ml-engine/docs/pricing),
          # even if no predictions are performed. There is additional cost for each
          # prediction performed.
          #
          # Unlike manual scaling, if the load gets too heavy for the nodes
          # that are up, the service will automatically add nodes to handle the
          # increased load as well as scale back as traffic drops, always maintaining
          # at least `min_nodes`. You will be charged for the time in which additional
          # nodes are used.
          #
          # If not specified, `min_nodes` defaults to 0, in which case, when traffic
          # to a model stops (and after a cool-down period), nodes will be shut down
          # and no charges will be incurred until traffic to the model resumes.
          #
          # You can set `min_nodes` when creating the model version, and you can also
          # update `min_nodes` for an existing version:
          # <pre>
          # update_body.json:
          # {
          #   'autoScaling': {
          #     'minNodes': 5
          #   }
          # }
          # </pre>
          # HTTP request:
          # <pre>
          # PATCH
          # https://ml.googleapis.com/v1/{name=projects/*/models/*/versions/*}?update_mask=autoScaling.minNodes
          # -d @./update_body.json
          # </pre>
    },
    "serviceAccount": "A String", # Optional. Specifies the service account for resource access control.
    "state": "A String", # Output only. The state of a version.
    "pythonVersion": "A String", # Optional. The version of Python used in prediction. If not set, the default
        # version is '2.7'. Python '3.5' is available when `runtime_version` is set
        # to '1.4' and above. Python '2.7' works with all supported runtime versions.
    "framework": "A String", # Optional. The machine learning framework AI Platform uses to train
        # this version of the model. Valid values are `TENSORFLOW`, `SCIKIT_LEARN`,
        # `XGBOOST`. If you do not specify a framework, AI Platform
        # will analyze files in the deployment_uri to determine a framework. If you
        # choose `SCIKIT_LEARN` or `XGBOOST`, you must also set the runtime version
        # of the model to 1.4 or greater.
        #
        # Do **not** specify a framework if you're deploying a [custom
        # prediction routine](/ml-engine/docs/tensorflow/custom-prediction-routines).
    "packageUris": [ # Optional. Cloud Storage paths (`gs://…`) of packages for [custom
        # prediction routines](/ml-engine/docs/tensorflow/custom-prediction-routines)
        # or [scikit-learn pipelines with custom
        # code](/ml-engine/docs/scikit/exporting-for-prediction#custom-pipeline-code).
        #
        # For a custom prediction routine, one of these packages must contain your
        # Predictor class (see
        # [`predictionClass`](#Version.FIELDS.prediction_class)). Additionally,
        # include any dependencies used by your Predictor or scikit-learn pipeline
        # uses that are not already included in your selected [runtime
        # version](/ml-engine/docs/tensorflow/runtime-version-list).
        #
        # If you specify this field, you must also set
        # [`runtimeVersion`](#Version.FIELDS.runtime_version) to 1.4 or greater.
      "A String",
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a model from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform model updates in order to avoid race
        # conditions: An `etag` is returned in the response to `GetVersion`, and
        # systems are expected to put that etag in the request to `UpdateVersion` to
        # ensure that their change will be applied to the model as intended.
    "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
    "deploymentUri": "A String", # Required. The Cloud Storage location of the trained model used to
        # create the version. See the
        # [guide to model
        # deployment](/ml-engine/docs/tensorflow/deploying-models) for more
        # information.
        #
        # 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.
        # The total number of model files can't exceed 1000.
    "createTime": "A String", # Output only. The time the version was created.
    "isDefault": True or False, # 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).
    "name": "A String", # Required.The name specified for the version when it was created.
        #
        # The version name must be unique within the model it is created in.
  },
  "onlinePredictionLogging": True or False, # Optional. If true, online prediction access logs are sent to StackDriver
      # Logging. These logs are like standard server access logs, containing
      # information like timestamp and latency for each request. Note that
      # [Stackdriver logs may incur a cost](/stackdriver/pricing), especially if
      # your project receives prediction requests at a high queries per second rate
      # (QPS). Estimate your costs before enabling this option.
      # 
      # Default is false.
  "name": "A String", # Required. The name specified for the model when it was created.
      # 
      # The model name must be unique within the project it is created in.
}

  updateMask: string, Required. Specifies the path, relative to `Model`, of the field to update.

For example, to change the description of a model to "foo" and set its
default version to "version_1", the `update_mask` parameter would be
specified as `description`, `default_version.name`, and the `PATCH`
request body would specify the new value, as follows:
    {
      "description": "foo",
      "defaultVersion": {
        "name":"version_1"
      }
    }

Currently the supported update masks are `description` and
`default_version.name`.
  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.
    "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.
    },
    "error": { # The `Status` type defines a logical error model that is suitable for # The error result of the operation in case of failure or cancellation.
        # 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).
      "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.
      "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.
        },
      ],
    },
    "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.
    "response": { # 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`.
      "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}`.
  }</pre>
</div>

<div class="method">
    <code class="details" id="setIamPolicy">setIamPolicy(resource, body, x__xgafv=None)</code>
  <pre>Sets the access control policy on the specified resource. Replaces any
existing policy.

Args:
  resource: string, REQUIRED: The resource for which the policy is being specified.
See the operation documentation for the appropriate value for this field. (required)
  body: object, The request body. (required)
    The object takes the form of:

{ # Request message for `SetIamPolicy` method.
    "policy": { # Defines an Identity and Access Management (IAM) policy. It is used to # REQUIRED: The complete policy to be applied to the `resource`. The size of
        # the policy is limited to a few 10s of KB. An empty policy is a
        # valid policy but certain Cloud Platform services (such as Projects)
        # might reject them.
        # specify access control policies for Cloud Platform resources.
        #
        #
        # A `Policy` consists of a list of `bindings`. A `binding` binds a list of
        # `members` to a `role`, where the members can be user accounts, Google groups,
        # Google domains, and service accounts. A `role` is a named list of permissions
        # defined by IAM.
        #
        # **JSON Example**
        #
        #     {
        #       "bindings": [
        #         {
        #           "role": "roles/owner",
        #           "members": [
        #             "user:mike@example.com",
        #             "group:admins@example.com",
        #             "domain:google.com",
        #             "serviceAccount:my-other-app@appspot.gserviceaccount.com"
        #           ]
        #         },
        #         {
        #           "role": "roles/viewer",
        #           "members": ["user:sean@example.com"]
        #         }
        #       ]
        #     }
        #
        # **YAML Example**
        #
        #     bindings:
        #     - members:
        #       - user:mike@example.com
        #       - group:admins@example.com
        #       - domain:google.com
        #       - serviceAccount:my-other-app@appspot.gserviceaccount.com
        #       role: roles/owner
        #     - members:
        #       - user:sean@example.com
        #       role: roles/viewer
        #
        #
        # For a description of IAM and its features, see the
        # [IAM developer's guide](https://cloud.google.com/iam/docs).
      "bindings": [ # Associates a list of `members` to a `role`.
          # `bindings` with no members will result in an error.
        { # Associates `members` with a `role`.
          "role": "A String", # Role that is assigned to `members`.
              # For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
          "members": [ # Specifies the identities requesting access for a Cloud Platform resource.
              # `members` can have the following values:
              #
              # * `allUsers`: A special identifier that represents anyone who is
              #    on the internet; with or without a Google account.
              #
              # * `allAuthenticatedUsers`: A special identifier that represents anyone
              #    who is authenticated with a Google account or a service account.
              #
              # * `user:{emailid}`: An email address that represents a specific Google
              #    account. For example, `alice@gmail.com` .
              #
              #
              # * `serviceAccount:{emailid}`: An email address that represents a service
              #    account. For example, `my-other-app@appspot.gserviceaccount.com`.
              #
              # * `group:{emailid}`: An email address that represents a Google group.
              #    For example, `admins@example.com`.
              #
              #
              # * `domain:{domain}`: The G Suite domain (primary) that represents all the
              #    users of that domain. For example, `google.com` or `example.com`.
              #
            "A String",
          ],
          "condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
              # NOTE: An unsatisfied condition will not allow user access via current
              # binding. Different bindings, including their conditions, are examined
              # independently.
              #
              #     title: "User account presence"
              #     description: "Determines whether the request has a user account"
              #     expression: "size(request.user) > 0"
            "description": "A String", # An optional description of the expression. This is a longer text which
                # describes the expression, e.g. when hovered over it in a UI.
            "expression": "A String", # Textual representation of an expression in
                # Common Expression Language syntax.
                #
                # The application context of the containing message determines which
                # well-known feature set of CEL is supported.
            "location": "A String", # An optional string indicating the location of the expression for error
                # reporting, e.g. a file name and a position in the file.
            "title": "A String", # An optional title for the expression, i.e. a short string describing
                # its purpose. This can be used e.g. in UIs which allow to enter the
                # expression.
          },
        },
      ],
      "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
          # prevent simultaneous updates of a policy from overwriting each other.
          # It is strongly suggested that systems make use of the `etag` in the
          # read-modify-write cycle to perform policy updates in order to avoid race
          # conditions: An `etag` is returned in the response to `getIamPolicy`, and
          # systems are expected to put that etag in the request to `setIamPolicy` to
          # ensure that their change will be applied to the same version of the policy.
          #
          # If no `etag` is provided in the call to `setIamPolicy`, then the existing
          # policy is overwritten blindly.
      "version": 42, # Deprecated.
      "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
        { # Specifies the audit configuration for a service.
            # The configuration determines which permission types are logged, and what
            # identities, if any, are exempted from logging.
            # An AuditConfig must have one or more AuditLogConfigs.
            #
            # If there are AuditConfigs for both `allServices` and a specific service,
            # the union of the two AuditConfigs is used for that service: the log_types
            # specified in each AuditConfig are enabled, and the exempted_members in each
            # AuditLogConfig are exempted.
            #
            # Example Policy with multiple AuditConfigs:
            #
            #     {
            #       "audit_configs": [
            #         {
            #           "service": "allServices"
            #           "audit_log_configs": [
            #             {
            #               "log_type": "DATA_READ",
            #               "exempted_members": [
            #                 "user:foo@gmail.com"
            #               ]
            #             },
            #             {
            #               "log_type": "DATA_WRITE",
            #             },
            #             {
            #               "log_type": "ADMIN_READ",
            #             }
            #           ]
            #         },
            #         {
            #           "service": "fooservice.googleapis.com"
            #           "audit_log_configs": [
            #             {
            #               "log_type": "DATA_READ",
            #             },
            #             {
            #               "log_type": "DATA_WRITE",
            #               "exempted_members": [
            #                 "user:bar@gmail.com"
            #               ]
            #             }
            #           ]
            #         }
            #       ]
            #     }
            #
            # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
            # logging. It also exempts foo@gmail.com from DATA_READ logging, and
            # bar@gmail.com from DATA_WRITE logging.
          "auditLogConfigs": [ # The configuration for logging of each type of permission.
            { # Provides the configuration for logging a type of permissions.
                # Example:
                #
                #     {
                #       "audit_log_configs": [
                #         {
                #           "log_type": "DATA_READ",
                #           "exempted_members": [
                #             "user:foo@gmail.com"
                #           ]
                #         },
                #         {
                #           "log_type": "DATA_WRITE",
                #         }
                #       ]
                #     }
                #
                # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
                # foo@gmail.com from DATA_READ logging.
              "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
                  # permission.
                  # Follows the same format of Binding.members.
                "A String",
              ],
              "logType": "A String", # The log type that this config enables.
            },
          ],
          "service": "A String", # Specifies a service that will be enabled for audit logging.
              # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
              # `allServices` is a special value that covers all services.
        },
      ],
    },
    "updateMask": "A String", # OPTIONAL: A FieldMask specifying which fields of the policy to modify. Only
        # the fields in the mask will be modified. If no mask is provided, the
        # following default mask is used:
        # paths: "bindings, etag"
        # This field is only used by Cloud IAM.
  }

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

Returns:
  An object of the form:

    { # Defines an Identity and Access Management (IAM) policy. It is used to
      # specify access control policies for Cloud Platform resources.
      #
      #
      # A `Policy` consists of a list of `bindings`. A `binding` binds a list of
      # `members` to a `role`, where the members can be user accounts, Google groups,
      # Google domains, and service accounts. A `role` is a named list of permissions
      # defined by IAM.
      #
      # **JSON Example**
      #
      #     {
      #       "bindings": [
      #         {
      #           "role": "roles/owner",
      #           "members": [
      #             "user:mike@example.com",
      #             "group:admins@example.com",
      #             "domain:google.com",
      #             "serviceAccount:my-other-app@appspot.gserviceaccount.com"
      #           ]
      #         },
      #         {
      #           "role": "roles/viewer",
      #           "members": ["user:sean@example.com"]
      #         }
      #       ]
      #     }
      #
      # **YAML Example**
      #
      #     bindings:
      #     - members:
      #       - user:mike@example.com
      #       - group:admins@example.com
      #       - domain:google.com
      #       - serviceAccount:my-other-app@appspot.gserviceaccount.com
      #       role: roles/owner
      #     - members:
      #       - user:sean@example.com
      #       role: roles/viewer
      #
      #
      # For a description of IAM and its features, see the
      # [IAM developer's guide](https://cloud.google.com/iam/docs).
    "bindings": [ # Associates a list of `members` to a `role`.
        # `bindings` with no members will result in an error.
      { # Associates `members` with a `role`.
        "role": "A String", # Role that is assigned to `members`.
            # For example, `roles/viewer`, `roles/editor`, or `roles/owner`.
        "members": [ # Specifies the identities requesting access for a Cloud Platform resource.
            # `members` can have the following values:
            #
            # * `allUsers`: A special identifier that represents anyone who is
            #    on the internet; with or without a Google account.
            #
            # * `allAuthenticatedUsers`: A special identifier that represents anyone
            #    who is authenticated with a Google account or a service account.
            #
            # * `user:{emailid}`: An email address that represents a specific Google
            #    account. For example, `alice@gmail.com` .
            #
            #
            # * `serviceAccount:{emailid}`: An email address that represents a service
            #    account. For example, `my-other-app@appspot.gserviceaccount.com`.
            #
            # * `group:{emailid}`: An email address that represents a Google group.
            #    For example, `admins@example.com`.
            #
            #
            # * `domain:{domain}`: The G Suite domain (primary) that represents all the
            #    users of that domain. For example, `google.com` or `example.com`.
            #
          "A String",
        ],
        "condition": { # Represents an expression text. Example: # The condition that is associated with this binding.
            # NOTE: An unsatisfied condition will not allow user access via current
            # binding. Different bindings, including their conditions, are examined
            # independently.
            #
            #     title: "User account presence"
            #     description: "Determines whether the request has a user account"
            #     expression: "size(request.user) > 0"
          "description": "A String", # An optional description of the expression. This is a longer text which
              # describes the expression, e.g. when hovered over it in a UI.
          "expression": "A String", # Textual representation of an expression in
              # Common Expression Language syntax.
              #
              # The application context of the containing message determines which
              # well-known feature set of CEL is supported.
          "location": "A String", # An optional string indicating the location of the expression for error
              # reporting, e.g. a file name and a position in the file.
          "title": "A String", # An optional title for the expression, i.e. a short string describing
              # its purpose. This can be used e.g. in UIs which allow to enter the
              # expression.
        },
      },
    ],
    "etag": "A String", # `etag` is used for optimistic concurrency control as a way to help
        # prevent simultaneous updates of a policy from overwriting each other.
        # It is strongly suggested that systems make use of the `etag` in the
        # read-modify-write cycle to perform policy updates in order to avoid race
        # conditions: An `etag` is returned in the response to `getIamPolicy`, and
        # systems are expected to put that etag in the request to `setIamPolicy` to
        # ensure that their change will be applied to the same version of the policy.
        #
        # If no `etag` is provided in the call to `setIamPolicy`, then the existing
        # policy is overwritten blindly.
    "version": 42, # Deprecated.
    "auditConfigs": [ # Specifies cloud audit logging configuration for this policy.
      { # Specifies the audit configuration for a service.
          # The configuration determines which permission types are logged, and what
          # identities, if any, are exempted from logging.
          # An AuditConfig must have one or more AuditLogConfigs.
          #
          # If there are AuditConfigs for both `allServices` and a specific service,
          # the union of the two AuditConfigs is used for that service: the log_types
          # specified in each AuditConfig are enabled, and the exempted_members in each
          # AuditLogConfig are exempted.
          #
          # Example Policy with multiple AuditConfigs:
          #
          #     {
          #       "audit_configs": [
          #         {
          #           "service": "allServices"
          #           "audit_log_configs": [
          #             {
          #               "log_type": "DATA_READ",
          #               "exempted_members": [
          #                 "user:foo@gmail.com"
          #               ]
          #             },
          #             {
          #               "log_type": "DATA_WRITE",
          #             },
          #             {
          #               "log_type": "ADMIN_READ",
          #             }
          #           ]
          #         },
          #         {
          #           "service": "fooservice.googleapis.com"
          #           "audit_log_configs": [
          #             {
          #               "log_type": "DATA_READ",
          #             },
          #             {
          #               "log_type": "DATA_WRITE",
          #               "exempted_members": [
          #                 "user:bar@gmail.com"
          #               ]
          #             }
          #           ]
          #         }
          #       ]
          #     }
          #
          # For fooservice, this policy enables DATA_READ, DATA_WRITE and ADMIN_READ
          # logging. It also exempts foo@gmail.com from DATA_READ logging, and
          # bar@gmail.com from DATA_WRITE logging.
        "auditLogConfigs": [ # The configuration for logging of each type of permission.
          { # Provides the configuration for logging a type of permissions.
              # Example:
              #
              #     {
              #       "audit_log_configs": [
              #         {
              #           "log_type": "DATA_READ",
              #           "exempted_members": [
              #             "user:foo@gmail.com"
              #           ]
              #         },
              #         {
              #           "log_type": "DATA_WRITE",
              #         }
              #       ]
              #     }
              #
              # This enables 'DATA_READ' and 'DATA_WRITE' logging, while exempting
              # foo@gmail.com from DATA_READ logging.
            "exemptedMembers": [ # Specifies the identities that do not cause logging for this type of
                # permission.
                # Follows the same format of Binding.members.
              "A String",
            ],
            "logType": "A String", # The log type that this config enables.
          },
        ],
        "service": "A String", # Specifies a service that will be enabled for audit logging.
            # For example, `storage.googleapis.com`, `cloudsql.googleapis.com`.
            # `allServices` is a special value that covers all services.
      },
    ],
  }</pre>
</div>

<div class="method">
    <code class="details" id="testIamPermissions">testIamPermissions(resource, body, x__xgafv=None)</code>
  <pre>Returns permissions that a caller has on the specified resource.
If the resource does not exist, this will return an empty set of
permissions, not a NOT_FOUND error.

Note: This operation is designed to be used for building permission-aware
UIs and command-line tools, not for authorization checking. This operation
may "fail open" without warning.

Args:
  resource: string, REQUIRED: The resource for which the policy detail is being requested.
See the operation documentation for the appropriate value for this field. (required)
  body: object, The request body. (required)
    The object takes the form of:

{ # Request message for `TestIamPermissions` method.
    "permissions": [ # The set of permissions to check for the `resource`. Permissions with
        # wildcards (such as '*' or 'storage.*') are not allowed. For more
        # information see
        # [IAM Overview](https://cloud.google.com/iam/docs/overview#permissions).
      "A String",
    ],
  }

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

Returns:
  An object of the form:

    { # Response message for `TestIamPermissions` method.
    "permissions": [ # A subset of `TestPermissionsRequest.permissions` that the caller is
        # allowed.
      "A String",
    ],
  }</pre>
</div>

</body></html>