class_docstr: Create Custom Training job.
  
  :param project_id: Required. The ID of the Google Cloud project that the service belongs to.
  :param region: Required. The ID of the Google Cloud region that the service belongs to.
  :param display_name: Required. The user-defined name of this TrainingPipeline.
  :param script_path: Required. Local path to training script.
  :param container_uri: Required: Uri of the training container image in the GCR.
  :param requirements: List of python packages dependencies of script.
  :param model_serving_container_image_uri: If the training produces a managed Vertex AI Model, the URI
      of the Model serving container suitable for serving the model produced by the
      training script.
  :param model_serving_container_predict_route: If the training produces a managed Vertex AI Model, An
      HTTP path to send prediction requests to the container, and which must be supported
      by it. If not specified a default HTTP path will be used by Vertex AI.
  :param model_serving_container_health_route: If the training produces a managed Vertex AI Model, an
      HTTP path to send health check requests to the container, and which must be supported
      by it. If not specified a standard HTTP path will be used by AI Platform.
  :param model_serving_container_command: The command with which the container is run. Not executed
      within a shell. The Docker image's ENTRYPOINT is used if this is not provided.
      Variable references $(VAR_NAME) are expanded using the container's
      environment. If a variable cannot be resolved, the reference in the
      input string will be unchanged. The $(VAR_NAME) syntax can be escaped
      with a double $$, ie: $$(VAR_NAME). Escaped references will never be
      expanded, regardless of whether the variable exists or not.
  :param model_serving_container_args: The arguments to the command. The Docker image's CMD is used if
      this is not provided. Variable references $(VAR_NAME) are expanded using the
      container's environment. If a variable cannot be resolved, the reference
      in the input string will be unchanged. The $(VAR_NAME) syntax can be
      escaped with a double $$, ie: $$(VAR_NAME). Escaped references will
      never be expanded, regardless of whether the variable exists or not.
  :param model_serving_container_environment_variables: The environment variables that are to be
      present in the container. Should be a dictionary where keys are environment variable names
      and values are environment variable values for those names.
  :param model_serving_container_ports: Declaration of ports that are exposed by the container. This
      field is primarily informational, it gives Vertex AI information about the
      network connections the container uses. Listing or not a port here has
      no impact on whether the port is actually exposed, any port listening on
      the default "0.0.0.0" address inside a container will be accessible from
      the network.
  :param model_description: The description of the Model.
  :param model_instance_schema_uri: Optional. Points to a YAML file stored on Google Cloud
      Storage describing the format of a single instance, which
      are used in
      ``PredictRequest.instances``,
      ``ExplainRequest.instances``
      and
      ``BatchPredictionJob.input_config``.
      The schema is defined as an OpenAPI 3.0.2 `Schema
      Object <https://tinyurl.com/y538mdwt#schema-object>`__.
      AutoML Models always have this field populated by AI
      Platform. Note: The URI given on output will be immutable
      and probably different, including the URI scheme, than the
      one given on input. The output URI will point to a location
      where the user only has a read access.
  :param model_parameters_schema_uri: Optional. Points to a YAML file stored on Google Cloud
      Storage describing the parameters of prediction and
      explanation via
      ``PredictRequest.parameters``,
      ``ExplainRequest.parameters``
      and
      ``BatchPredictionJob.model_parameters``.
      The schema is defined as an OpenAPI 3.0.2 `Schema
      Object <https://tinyurl.com/y538mdwt#schema-object>`__.
      AutoML Models always have this field populated by AI
      Platform, if no parameters are supported it is set to an
      empty string. Note: The URI given on output will be
      immutable and probably different, including the URI scheme,
      than the one given on input. The output URI will point to a
      location where the user only has a read access.
  :param model_prediction_schema_uri: Optional. Points to a YAML file stored on Google Cloud
      Storage describing the format of a single prediction
      produced by this Model, which are returned via
      ``PredictResponse.predictions``,
      ``ExplainResponse.explanations``,
      and
      ``BatchPredictionJob.output_config``.
      The schema is defined as an OpenAPI 3.0.2 `Schema
      Object <https://tinyurl.com/y538mdwt#schema-object>`__.
      AutoML Models always have this field populated by AI
      Platform. Note: The URI given on output will be immutable
      and probably different, including the URI scheme, than the
      one given on input. The output URI will point to a location
      where the user only has a read access.
  :param parent_model: Optional. The resource name or model ID of an existing model.
      The new model uploaded by this job will be a version of `parent_model`.
      Only set this field when training a new version of an existing model.
  :param is_default_version: Optional. When set to True, the newly uploaded model version will
      automatically have alias "default" included. Subsequent uses of
      the model produced by this job without a version specified will
      use this "default" version.
      When set to False, the "default" alias will not be moved.
      Actions targeting the model version produced by this job will need
      to specifically reference this version by ID or alias.
      New model uploads, i.e. version 1, will always be "default" aliased.
  :param model_version_aliases: Optional. User provided version aliases so that the model version
      uploaded by this job can be referenced via alias instead of
      auto-generated version ID. A default version alias will be created
      for the first version of the model.
      The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9]
  :param model_version_description: Optional. The description of the model version
      being uploaded by this job.
  :param project_id: Project to run training in.
  :param region: Location to run training in.
  :param labels: Optional. The labels with user-defined metadata to
      organize TrainingPipelines.
      Label keys and values can be no longer than 64
      characters, can only
      contain lowercase letters, numeric characters,
      underscores and dashes. International characters
      are allowed.
      See https://goo.gl/xmQnxf for more information
      and examples of labels.
  :param training_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
      managed encryption key used to protect the training pipeline. Has the
      form:
      ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
      The key needs to be in the same region as where the compute
      resource is created.
  
      If set, this TrainingPipeline will be secured by this key.
  
      Note: Model trained by this TrainingPipeline is also secured
      by this key if ``model_to_upload`` is not set separately.
  :param model_encryption_spec_key_name: Optional. The Cloud KMS resource identifier of the customer
      managed encryption key used to protect the model. Has the
      form:
      ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
      The key needs to be in the same region as where the compute
      resource is created.
  
      If set, the trained Model will be secured by this key.
  :param staging_bucket: Bucket used to stage source and training artifacts.
  :param dataset: Vertex AI to fit this training against.
  :param annotation_schema_uri: Google Cloud Storage URI points to a YAML file describing
      annotation schema. The schema is defined as an OpenAPI 3.0.2
      [Schema Object]
      (https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schema-object)
  
      Only Annotations that both match this schema and belong to
      DataItems not ignored by the split method are used in
      respectively training, validation or test role, depending on
      the role of the DataItem they are on.
  
      When used in conjunction with
      ``annotations_filter``,
      the Annotations used for training are filtered by both
      ``annotations_filter``
      and
      ``annotation_schema_uri``.
  :param model_display_name: If the script produces a managed Vertex AI Model. The display name of
          the Model. The name can be up to 128 characters long and can be consist
          of any UTF-8 characters.
  
          If not provided upon creation, the job's display_name is used.
  :param model_labels: Optional. The labels with user-defined metadata to
          organize your Models.
          Label keys and values can be no longer than 64
          characters, can only
          contain lowercase letters, numeric characters,
          underscores and dashes. International characters
          are allowed.
          See https://goo.gl/xmQnxf for more information
          and examples of labels.
  :param base_output_dir: GCS output directory of job. If not provided a timestamped directory in the
      staging directory will be used.
  
      Vertex AI sets the following environment variables when it runs your training code:
  
      -  AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts,
          i.e. <base_output_dir>/model/
      -  AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints,
          i.e. <base_output_dir>/checkpoints/
      -  AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard
          logs, i.e. <base_output_dir>/logs/
  :param service_account: Specifies the service account for workload run-as account.
          Users submitting jobs must have act-as permission on this run-as account.
  :param network: The full name of the Compute Engine network to which the job
          should be peered.
          Private services access must already be configured for the network.
          If left unspecified, the job is not peered with any network.
  :param bigquery_destination: Provide this field if `dataset` is a BiqQuery dataset.
          The BigQuery project location where the training data is to
          be written to. In the given project a new dataset is created
          with name
          ``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``
          where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All
          training input data will be written into that dataset. In
          the dataset three tables will be created, ``training``,
          ``validation`` and ``test``.
  
          -  AIP_DATA_FORMAT = "bigquery".
          -  AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"
          -  AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"
          -  AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
  :param args: Command line arguments to be passed to the Python script.
  :param environment_variables: Environment variables to be passed to the container.
          Should be a dictionary where keys are environment variable names
          and values are environment variable values for those names.
          At most 10 environment variables can be specified.
          The Name of the environment variable must be unique.
  :param replica_count: The number of worker replicas. If replica count = 1 then one chief
          replica will be provisioned. If replica_count > 1 the remainder will be
          provisioned as a worker replica pool.
  :param machine_type: The type of machine to use for training.
  :param accelerator_type: Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED,
          NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4,
          NVIDIA_TESLA_T4
  :param accelerator_count: The number of accelerators to attach to a worker replica.
  :param boot_disk_type: Type of the boot disk, default is `pd-ssd`.
          Valid values: `pd-ssd` (Persistent Disk Solid State Drive) or
          `pd-standard` (Persistent Disk Hard Disk Drive).
  :param boot_disk_size_gb: Size in GB of the boot disk, default is 100GB.
          boot disk size must be within the range of [100, 64000].
  :param training_fraction_split: Optional. The fraction of the input data that is to be used to train
          the Model. This is ignored if Dataset is not provided.
  :param validation_fraction_split: Optional. The fraction of the input data that is to be used to
      validate the Model. This is ignored if Dataset is not provided.
  :param test_fraction_split: Optional. The fraction of the input data that is to be used to evaluate
          the Model. This is ignored if Dataset is not provided.
  :param training_filter_split: Optional. 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. This is ignored if Dataset is not provided.
  :param validation_filter_split: Optional. 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. This is ignored if Dataset is not provided.
  :param test_filter_split: Optional. 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. This is ignored if Dataset is not provided.
  :param predefined_split_column_name: Optional. The key is a name of one of the Dataset's data
          columns. The value of the key (either the label's value or
          value in the column) must be one of {``training``,
          ``validation``, ``test``}, and it defines to which set the
          given piece of data is assigned. If for a piece of data the
          key is not present or has an invalid value, that piece is
          ignored by the pipeline.
  
          Supported only for tabular and time series Datasets.
  :param timestamp_split_column_name: Optional. The key is a name of one of the Dataset's data
          columns. The value of the key values of the key (the values in
          the column) must be in RFC 3339 `date-time` format, where
          `time-offset` = `"Z"` (e.g. 1985-04-12T23:20:50.52Z). If for a
          piece of data the key is not present or has an invalid value,
          that piece is ignored by the pipeline.
  
          Supported only for tabular and time series Datasets.
  :param tensorboard: Optional. The name of a Vertex AI resource to which this CustomJob will upload
          logs. Format:
          ``projects/{project}/locations/{location}/tensorboards/{tensorboard}``
          For more information on configuring your service account please visit:
          https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training
  :param sync: Whether to execute the AI Platform job synchronously. If False, this method
          will be executed in concurrent Future and any downstream object will
          be immediately returned and synced when the Future has completed.
  :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
  :param impersonation_chain: Optional service account to impersonate using short-term
      credentials, or chained list of accounts required to get the access_token
      of the last account in the list, which will be impersonated in the request.
      If set as a string, the account must grant the originating account
      the Service Account Token Creator IAM role.
      If set as a sequence, the identities from the list must grant
      Service Account Token Creator IAM role to the directly preceding identity, with first
      account from the list granting this role to the originating account (templated).
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docstr: Callback called when the operator is killed; cancel any running job.
docstr_tok: ["Callback","called","when","the","operator","is","killed",";","cancel","any","running","job","."]
code: def on_kill(self) -> None:
      """Callback called when the operator is killed; cancel any running job."""
      if self.hook:
          self.hook.cancel_job()
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Sequence
  from google.api_core.exceptions import NotFound
  from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
  from google.cloud.aiplatform.models import Model
  from google.cloud.aiplatform_v1.types.dataset import Dataset
  from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
  from airflow.providers.google.cloud.hooks.vertex_ai.custom_job import CustomJobHook
  from airflow.providers.google.cloud.links.vertex_ai import VertexAIModelLink, VertexAITrainingLink, VertexAITrainingPipelinesLink
  from airflow.providers.google.cloud.operators.cloud_base import GoogleCloudBaseOperator
label_desc: Use image node_id 3 for calling the CreateCustomTrainingJobOperator obj's underlying member method code with example usage: obj.on_kill() without return types