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| <h1><a href="notebooks_v1.html">Notebooks API</a> . <a href="notebooks_v1.projects.html">projects</a> . <a href="notebooks_v1.projects.locations.html">locations</a> . <a href="notebooks_v1.projects.locations.executions.html">executions</a></h1> |
| <h2>Instance Methods</h2> |
| <p class="toc_element"> |
| <code><a href="#close">close()</a></code></p> |
| <p class="firstline">Close httplib2 connections.</p> |
| <p class="toc_element"> |
| <code><a href="#create">create(parent, body=None, executionId=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Creates a new Execution in a given project and location.</p> |
| <p class="toc_element"> |
| <code><a href="#delete">delete(name, x__xgafv=None)</a></code></p> |
| <p class="firstline">Deletes execution</p> |
| <p class="toc_element"> |
| <code><a href="#get">get(name, x__xgafv=None)</a></code></p> |
| <p class="firstline">Gets details of executions</p> |
| <p class="toc_element"> |
| <code><a href="#list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</a></code></p> |
| <p class="firstline">Lists executions in a given project and location</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> |
| <h3>Method Details</h3> |
| <div class="method"> |
| <code class="details" id="close">close()</code> |
| <pre>Close httplib2 connections.</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="create">create(parent, body=None, executionId=None, x__xgafv=None)</code> |
| <pre>Creates a new Execution in a given project and location. |
| |
| Args: |
| parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required) |
| body: object, The request body. |
| The object takes the form of: |
| |
| { # The definition of a single executed notebook. |
| "createTime": "A String", # Output only. Time the Execution was instantiated. |
| "description": "A String", # A brief description of this execution. |
| "displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'. |
| "executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc. |
| "acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution. |
| "coreCount": "A String", # Count of cores of this accelerator. |
| "type": "A String", # Type of this accelerator. |
| }, |
| "containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container |
| "dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions. |
| "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}` |
| }, |
| "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb` |
| "jobType": "A String", # The type of Job to be used on this execution. |
| "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file. |
| "labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions. |
| "a_key": "A String", |
| }, |
| "masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine). |
| "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks` |
| "parameters": "A String", # Parameters used within the 'input_notebook_file' notebook. |
| "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml` |
| "scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported. |
| "serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. |
| "vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions. |
| "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/ |
| "a_key": "A String", |
| }, |
| "network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. |
| }, |
| }, |
| "jobUri": "A String", # Output only. The URI of the external job used to execute the notebook. |
| "name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` |
| "outputNotebookFile": "A String", # Output notebook file generated by this execution |
| "state": "A String", # Output only. State of the underlying AI Platform job. |
| "updateTime": "A String", # Output only. Time the Execution was last updated. |
| } |
| |
| executionId: string, Required. User-defined unique ID of this execution. |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # This resource represents a long-running operation that is the result of a network API call. |
| "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. |
| "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. |
| "code": 42, # The status code, which should be an enum value of google.rpc.Code. |
| "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. |
| { |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| ], |
| "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. |
| }, |
| "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. |
| "response": { # The normal 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. |
| }, |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="delete">delete(name, x__xgafv=None)</code> |
| <pre>Deletes execution |
| |
| Args: |
| name: string, Required. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` (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. |
| "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. |
| "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. |
| "code": 42, # The status code, which should be an enum value of google.rpc.Code. |
| "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. |
| { |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| ], |
| "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. |
| }, |
| "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. |
| "a_key": "", # Properties of the object. Contains field @type with type URL. |
| }, |
| "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. |
| "response": { # The normal 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. |
| }, |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="get">get(name, x__xgafv=None)</code> |
| <pre>Gets details of executions |
| |
| Args: |
| name: string, Required. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` (required) |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # The definition of a single executed notebook. |
| "createTime": "A String", # Output only. Time the Execution was instantiated. |
| "description": "A String", # A brief description of this execution. |
| "displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'. |
| "executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc. |
| "acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution. |
| "coreCount": "A String", # Count of cores of this accelerator. |
| "type": "A String", # Type of this accelerator. |
| }, |
| "containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container |
| "dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions. |
| "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}` |
| }, |
| "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb` |
| "jobType": "A String", # The type of Job to be used on this execution. |
| "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file. |
| "labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions. |
| "a_key": "A String", |
| }, |
| "masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine). |
| "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks` |
| "parameters": "A String", # Parameters used within the 'input_notebook_file' notebook. |
| "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml` |
| "scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported. |
| "serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. |
| "vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions. |
| "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/ |
| "a_key": "A String", |
| }, |
| "network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. |
| }, |
| }, |
| "jobUri": "A String", # Output only. The URI of the external job used to execute the notebook. |
| "name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` |
| "outputNotebookFile": "A String", # Output notebook file generated by this execution |
| "state": "A String", # Output only. State of the underlying AI Platform job. |
| "updateTime": "A String", # Output only. Time the Execution was last updated. |
| }</pre> |
| </div> |
| |
| <div class="method"> |
| <code class="details" id="list">list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)</code> |
| <pre>Lists executions in a given project and location |
| |
| Args: |
| parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required) |
| filter: string, Filter applied to resulting executions. Currently only supports filtering executions by a specified schedule_id. Format: `schedule_id=` |
| orderBy: string, Sort by field. |
| pageSize: integer, Maximum return size of the list call. |
| pageToken: string, A previous returned page token that can be used to continue listing from the last result. |
| x__xgafv: string, V1 error format. |
| Allowed values |
| 1 - v1 error format |
| 2 - v2 error format |
| |
| Returns: |
| An object of the form: |
| |
| { # Response for listing scheduled notebook executions |
| "executions": [ # A list of returned instances. |
| { # The definition of a single executed notebook. |
| "createTime": "A String", # Output only. Time the Execution was instantiated. |
| "description": "A String", # A brief description of this execution. |
| "displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'. |
| "executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc. |
| "acceleratorConfig": { # Definition of a hardware accelerator. Note that not all combinations of `type` and `core_count` are valid. Check [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid combination. TPUs are not supported. # Configuration (count and accelerator type) for hardware running notebook execution. |
| "coreCount": "A String", # Count of cores of this accelerator. |
| "type": "A String", # Type of this accelerator. |
| }, |
| "containerImageUri": "A String", # Container Image URI to a DLVM Example: 'gcr.io/deeplearning-platform-release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container |
| "dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions. |
| "cluster": "A String", # URI for cluster used to run Dataproc execution. Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}` |
| }, |
| "inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: `gs://{bucket_name}/{folder}/{notebook_file_name}` Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb` |
| "jobType": "A String", # The type of Job to be used on this execution. |
| "kernelSpec": "A String", # Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file. |
| "labels": { # Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions. |
| "a_key": "A String", |
| }, |
| "masterType": "A String", # Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when `scaleTier` is set to `CUSTOM`. You can use certain Compute Engine machine types directly in this field. The following types are supported: - `n1-standard-4` - `n1-standard-8` - `n1-standard-16` - `n1-standard-32` - `n1-standard-64` - `n1-standard-96` - `n1-highmem-2` - `n1-highmem-4` - `n1-highmem-8` - `n1-highmem-16` - `n1-highmem-32` - `n1-highmem-64` - `n1-highmem-96` - `n1-highcpu-16` - `n1-highcpu-32` - `n1-highcpu-64` - `n1-highcpu-96` Alternatively, you can use the following legacy machine types: - `standard` - `large_model` - `complex_model_s` - `complex_model_m` - `complex_model_l` - `standard_gpu` - `complex_model_m_gpu` - `complex_model_l_gpu` - `standard_p100` - `complex_model_m_p100` - `standard_v100` - `large_model_v100` - `complex_model_m_v100` - `complex_model_l_v100` Finally, if you want to use a TPU for training, specify `cloud_tpu` in this field. Learn more about the [special configuration options for training with TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine). |
| "outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: `gs://{bucket_name}/{folder}` Ex: `gs://notebook_user/scheduled_notebooks` |
| "parameters": "A String", # Parameters used within the 'input_notebook_file' notebook. |
| "paramsYamlFile": "A String", # Parameters to be overridden in the notebook during execution. Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml` |
| "scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported. |
| "serviceAccount": "A String", # The email address of a service account to use when running the execution. You must have the `iam.serviceAccounts.actAs` permission for the specified service account. |
| "vertexAiParameters": { # Parameters used in Vertex AI JobType executions. # Parameters used in Vertex AI JobType executions. |
| "env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/ |
| "a_key": "A String", |
| }, |
| "network": "A String", # The full name of the Compute Engine [network](/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) is of the form `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network. |
| }, |
| }, |
| "jobUri": "A String", # Output only. The URI of the external job used to execute the notebook. |
| "name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` |
| "outputNotebookFile": "A String", # Output notebook file generated by this execution |
| "state": "A String", # Output only. State of the underlying AI Platform job. |
| "updateTime": "A String", # Output only. Time the Execution was last updated. |
| }, |
| ], |
| "nextPageToken": "A String", # Page token that can be used to continue listing from the last result in the next list call. |
| "unreachable": [ # Executions IDs that could not be reached. For example: ['projects/{project_id}/location/{location}/executions/imagenet_test1', 'projects/{project_id}/location/{location}/executions/classifier_train1'] |
| "A String", |
| ], |
| }</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> |
| |
| </body></html> |
