# Google Cloud Machine Learning Engine . projects . models

## Instance Methods

Returns the versions Resource.

Creates a model which will later contain one or more versions.

Deletes a model.

Gets information about a model, including its name, the description (if

Lists the models in a project.

Retrieves the next page of results.

### Method Details

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

    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.

    Authorization: requires `Editor` role on the specified project. (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.
        "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.
            # 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",
        ],
        "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).
          "description": "A String", # Optional. The description specified for the version when it was created.
          "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
              # If not set, Google Cloud ML will choose a version.
          "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
              # model. You should generally use `automatic_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.
          },
          "deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
              # create the version. See the
              # [overview of model
              # deployment](/ml-engine/docs/concepts/deployment-overview) for more
              # informaiton.
              #
              # When passing Version to
              # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)
              # the model service uses the specified location as the source of the model.
              # Once deployed, the model version is hosted by the prediction service, so
              # this location is useful only as a historical record.
              # The total number of model files can't exceed 1000.
          "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
          "automaticScaling": { # 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, so 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
                # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
          },
          "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.
        },
        "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.
        "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
            # Default is false.
        "description": "A String", # Optional. The description specified for the model when it was created.
      }

      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.
          "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.
              # 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",
          ],
          "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).
            "description": "A String", # Optional. The description specified for the version when it was created.
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
                # If not set, Google Cloud ML will choose a version.
            "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
                # model. You should generally use `automatic_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.
            },
            "deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
                # create the version. See the
                # [overview of model
                # deployment](/ml-engine/docs/concepts/deployment-overview) for more
                # informaiton.
                #
                # When passing Version to
                # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)
                # the model service uses the specified location as the source of the model.
                # Once deployed, the model version is hosted by the prediction service, so
                # this location is useful only as a historical record.
                # The total number of model files can't exceed 1000.
            "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
            "automaticScaling": { # 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, so 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
                  # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
            },
            "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.
          },
          "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.
          "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
              # Default is false.
          "description": "A String", # Optional. The description specified for the model when it was created.
        }

`delete(name, x__xgafv=None)`

    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.

    Authorization: requires `Editor` role on the parent project. (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 different # The error result of the operation in case of failure or cancellation.
            # programming environments, including REST APIs and RPC APIs. It is used by
            # [gRPC](https://github.com/grpc). The error model is designed to be:
            #
            # - Simple to use and understand for most users
            # - Flexible enough to meet unexpected needs
            #
            # # Overview
            #
            # The `Status` message contains three pieces of data: error code, error message,
            # and error details. The error code should be an enum value of
            # google.rpc.Code, but it may accept additional error codes if needed.  The
            # error message should be a developer-facing English message that helps
            # developers *understand* and *resolve* the error. If a localized user-facing
            # error message is needed, put the localized message in the error details or
            # localize it in the client. The optional error details may contain arbitrary
            # information about the error. There is a predefined set of error detail types
            # in the package `google.rpc` that can be used for common error conditions.
            #
            # # Language mapping
            #
            # The `Status` message is the logical representation of the error model, but it
            # is not necessarily the actual wire format. When the `Status` message is
            # exposed in different client libraries and different wire protocols, it can be
            # mapped differently. For example, it will likely be mapped to some exceptions
            # in Java, but more likely mapped to some error codes in C.
            #
            # # Other uses
            #
            # The error model and the `Status` message can be used in a variety of
            # environments, either with or without APIs, to provide a
            # consistent developer experience across different environments.
            #
            # Example uses of this error model include:
            #
            # - Partial errors. If a service needs to return partial errors to the client,
            #     it may embed the `Status` in the normal response to indicate the partial
            #     errors.
            #
            # - Workflow errors. A typical workflow has multiple steps. Each step may
            #     have a `Status` message for error reporting.
            #
            # - Batch operations. If a client uses batch request and batch response, the
            #     `Status` message should be used directly inside batch response, one for
            #     each error sub-response.
            #
            # - Asynchronous operations. If an API call embeds asynchronous operation
            #     results in its response, the status of those operations should be
            #     represented directly using the `Status` message.
            #
            # - Logging. If some API errors are stored in logs, the message `Status` could
            #     be used directly after any stripping needed for security/privacy reasons.
          "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 will be 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 have the format of `operations/some/unique/name`.
      }

`get(name, x__xgafv=None)`

    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.

    Authorization: requires `Viewer` role on the parent project. (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.
          "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.
              # 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",
          ],
          "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).
            "description": "A String", # Optional. The description specified for the version when it was created.
            "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
                # If not set, Google Cloud ML will choose a version.
            "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
                # model. You should generally use `automatic_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.
            },
            "deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
                # create the version. See the
                # [overview of model
                # deployment](/ml-engine/docs/concepts/deployment-overview) for more
                # informaiton.
                #
                # When passing Version to
                # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)
                # the model service uses the specified location as the source of the model.
                # Once deployed, the model version is hosted by the prediction service, so
                # this location is useful only as a historical record.
                # The total number of model files can't exceed 1000.
            "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
            "automaticScaling": { # 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, so 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
                  # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
            },
            "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.
          },
          "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.
          "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
              # Default is false.
          "description": "A String", # Optional. The description specified for the model when it was created.
        }

`list(parent, pageToken=None, x__xgafv=None, pageSize=None)`

    Lists the models in a project.

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

    Args:
      parent: string, Required. The name of the project whose models are to be listed.

    Authorization: requires `Viewer` role on the specified project. (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.

    Returns:
      An object of the form:

        { # Response message for the ListModels method.
        "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.
              "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.
                  # 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",
              ],
              "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).
                "description": "A String", # Optional. The description specified for the version when it was created.
                "runtimeVersion": "A String", # Optional. The Google Cloud ML runtime version to use for this deployment.
                    # If not set, Google Cloud ML will choose a version.
                "manualScaling": { # Options for manually scaling a model. # Manually select the number of nodes to use for serving the
                    # model. You should generally use `automatic_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.
                },
                "deploymentUri": "A String", # Required. The Google Cloud Storage location of the trained model used to
                    # create the version. See the
                    # [overview of model
                    # deployment](/ml-engine/docs/concepts/deployment-overview) for more
                    # informaiton.
                    #
                    # When passing Version to
                    # [projects.models.versions.create](/ml-engine/reference/rest/v1/projects.models.versions/create)
                    # the model service uses the specified location as the source of the model.
                    # Once deployed, the model version is hosted by the prediction service, so
                    # this location is useful only as a historical record.
                    # The total number of model files can't exceed 1000.
                "lastUseTime": "A String", # Output only. The time the version was last used for prediction.
                "automaticScaling": { # 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, so 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
                      # [pricing](https://cloud.google.com/ml-engine/pricing#prediction_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.
                },
                "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.
              },
              "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.
              "onlinePredictionLogging": True or False, # Optional. If true, enables StackDriver Logging for online prediction.
                  # Default is false.
              "description": "A String", # Optional. The description specified for the model when it was created.
            },
        ],
        "nextPageToken": "A String", # Optional. Pass this token as the `page_token` field of the request for a
            # subsequent call.
      }

`list_next(previous_request, previous_response)`

    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.