Training with a custom service account needs a new field named service account
trainingInput:
  serviceAccount: CUSTOM_SERVICE_ACCOUNT

https://cloud.google.com/ai-platform/training/docs/custom-service-account


airflow/providers/google/cloud/operators/mlengine.py
:param model_name: The name of the Google Cloud ML Engine model that the version
        belongs to. (templated)
    :type model_name: str
    :param gcp_conn_id: The connection ID to use when fetching connection info.
    :type gcp_conn_id: str
    :param project_id: The Google Cloud project name to which MLEngine model belongs.
        If set to None or missing, the default project_id from the Google Cloud connection is used.
        (templated)
    :type project_id: str
    :param delegate_to: The account to impersonate using domain-wide delegation of authority,
        if any. For this to work, the service account making the request must have
        domain-wide delegation enabled.
    :type delegate_to: str
    :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).
    :type impersonation_chain: Union[str, Sequence[str]]
    """
    template_fields = [
        '_project_id',
        '_model_name',
        '_impersonation_chain',
    ]
    @apply_defaults
    def __init__(