Create or update the resource policy for a configured audience model.
Parameters:
-
configured_audience_model_arn
(ConfiguredAudienceModelArn)
–
The Amazon Resource Name (ARN) of the configured audience model that the resource policy will govern.
-
configured_audience_model_policy
(ResourcePolicy)
–
-
previous_policy_hash
(Optional[Hash], default:
None
)
–
A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy.
-
policy_existence_condition
(Optional[PolicyExistenceCondition], default:
None
)
–
Use this to prevent unexpected concurrent modification of the policy.
Raises:
Source code in docs_src/capo_cleanroomsml/_services/clean_rooms_ml.py
| def put_configured_audience_model_policy(
self,
configured_audience_model_arn: "capo_cleanroomsml.types.configured_audience_model_arn.ConfiguredAudienceModelArn",
configured_audience_model_policy: "capo_cleanroomsml.types.resource_policy.ResourcePolicy",
*,
config_overrides: Optional[CleanRoomsMLClientConfig] = None,
previous_policy_hash: Optional["capo_cleanroomsml.types.hash.Hash"] = None,
policy_existence_condition: Optional[
"capo_cleanroomsml.types.policy_existence_condition.PolicyExistenceCondition"
] = None,
) -> "capo_cleanroomsml.types.put_configured_audience_model_policy_response.PutConfiguredAudienceModelPolicyResponse":
"""<p>Create or update the resource policy for a configured audience model.</p>
Args:
configured_audience_model_arn: <p>The Amazon Resource Name (ARN) of the configured audience model that the resource policy will govern.</p>
configured_audience_model_policy: <p>The IAM resource policy.</p>
previous_policy_hash: <p>A cryptographic hash of the contents of the policy used to prevent unexpected concurrent modification of the policy.</p>
policy_existence_condition: <p>Use this to prevent unexpected concurrent modification of the policy.</p>
Raises:
capo_cleanroomsml.errors.access_denied_exception.AccessDeniedException: <p>You do not have sufficient access to perform this action.</p>
capo_cleanroomsml.errors.resource_not_found_exception.ResourceNotFoundException: <p>The resource you are requesting does not exist.</p>
capo_cleanroomsml.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling.</p>
capo_cleanroomsml.errors.validation_exception.ValidationException: <p>The request parameters for this request are incorrect.</p>
capo_cleanroomsml.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_cleanroomsml.types.put_configured_audience_model_policy_request.PutConfiguredAudienceModelPolicyRequest]",
) -> OperationResponse[
"capo_cleanroomsml.types.put_configured_audience_model_policy_response.PutConfiguredAudienceModelPolicyResponse"
]:
import capo_cleanroomsml._operations.aws_stark_control_service.put_configured_audience_model_policy
output, http_response = (
capo_cleanroomsml._operations.aws_stark_control_service.put_configured_audience_model_policy.put_configured_audience_model_policy(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
interceptors_, options_ = self.operation_options(
config_overrides, OperationLogging("PutConfiguredAudienceModelPolicy")
)
input_: capo_cleanroomsml.types.put_configured_audience_model_policy_request.PutConfiguredAudienceModelPolicyRequest = {
"configured_audience_model_arn": configured_audience_model_arn,
"configured_audience_model_policy": configured_audience_model_policy,
}
if previous_policy_hash is not None:
input_["previous_policy_hash"] = previous_policy_hash
if policy_existence_condition is not None:
input_["policy_existence_condition"] = policy_existence_condition
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|