feat(aiplatform): update the api

#### aiplatform:v1

The following keys were deleted:
- schemas.GoogleCloudAiplatformV1ExportDataConfig.properties.filterSplit.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1ExportFilterSplit (Total Keys: 5)

The following keys were added:
- schemas.GoogleCloudAiplatformV1MachineSpec.properties.tpuTopology.type (Total Keys: 1)

#### aiplatform:v1beta1

The following keys were deleted:
- schemas.GoogleCloudAiplatformV1beta1ExportDataConfig.properties.filterSplit.$ref (Total Keys: 1)
- schemas.GoogleCloudAiplatformV1beta1ExportFilterSplit (Total Keys: 5)

The following keys were added:
- schemas.GoogleCloudAiplatformV1beta1MachineSpec.properties.tpuTopology.type (Total Keys: 1)

docs/dyn/aiplatform_v1.projects.locations.batchPredictionJobs.html

@@ -147,6 +147,7 @@ <h3>Method Details</h3>
       &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
       &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
       &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+      &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
     },
     &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
     &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
@@ -391,6 +392,7 @@ <h3>Method Details</h3>
       &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
       &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
       &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+      &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
     },
     &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
     &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
@@ -677,6 +679,7 @@ <h3>Method Details</h3>
       &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
       &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
       &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+      &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
     },
     &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
     &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count
@@ -934,6 +937,7 @@ <h3>Method Details</h3>
           &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
           &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
           &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
         },
         &quot;maxReplicaCount&quot;: 42, # Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
         &quot;startingReplicaCount&quot;: 42, # Immutable. The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than max_replica_count

docs/dyn/aiplatform_v1.projects.locations.customJobs.html

@@ -199,6 +199,7 @@ <h3>Method Details</h3>
           &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
           &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
           &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
         },
         &quot;nfsMounts&quot;: [ # Optional. List of NFS mount spec.
           { # Represents a mount configuration for Network File System (NFS) to mount.
@@ -307,6 +308,7 @@ <h3>Method Details</h3>
           &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
           &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
           &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
         },
         &quot;nfsMounts&quot;: [ # Optional. List of NFS mount spec.
           { # Represents a mount configuration for Network File System (NFS) to mount.
@@ -457,6 +459,7 @@ <h3>Method Details</h3>
           &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
           &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
           &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+          &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
         },
         &quot;nfsMounts&quot;: [ # Optional. List of NFS mount spec.
           { # Represents a mount configuration for Network File System (NFS) to mount.
@@ -578,6 +581,7 @@ <h3>Method Details</h3>
               &quot;acceleratorCount&quot;: 42, # The number of accelerators to attach to the machine.
               &quot;acceleratorType&quot;: &quot;A String&quot;, # Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
               &quot;machineType&quot;: &quot;A String&quot;, # Immutable. The type of the machine. See the [list of machine types supported for prediction](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types) See the [list of machine types supported for custom training](https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types). For DeployedModel this field is optional, and the default value is `n1-standard-2`. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
+              &quot;tpuTopology&quot;: &quot;A String&quot;, # Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: &quot;2x2x1&quot;).
             },
             &quot;nfsMounts&quot;: [ # Optional. List of NFS mount spec.
               { # Represents a mount configuration for Network File System (NFS) to mount.

docs/dyn/aiplatform_v1.projects.locations.datasets.html

@@ -256,11 +256,6 @@ <h3>Method Details</h3>
 { # Request message for DatasetService.ExportData.
   &quot;exportConfig&quot;: { # Describes what part of the Dataset is to be exported, the destination of the export and how to export. # Required. The desired output location.
     &quot;annotationsFilter&quot;: &quot;A String&quot;, # An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations.
-    &quot;filterSplit&quot;: { # Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as &#x27;-&#x27; (the minus sign). Supported only for unstructured Datasets. # Split based on the provided filters for each set.
-      &quot;testFilter&quot;: &quot;A String&quot;, # Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
-      &quot;trainingFilter&quot;: &quot;A String&quot;, # Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
-      &quot;validationFilter&quot;: &quot;A String&quot;, # Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
-    },
     &quot;fractionSplit&quot;: { # Assigns the input data to training, validation, and test sets as per the given fractions. Any of `training_fraction`, `validation_fraction` and `test_fraction` may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test. # Split based on fractions defining the size of each set.
       &quot;testFraction&quot;: 3.14, # The fraction of the input data that is to be used to evaluate the Model.
       &quot;trainingFraction&quot;: 3.14, # The fraction of the input data that is to be used to train the Model.