How to use the flytekit.sdk.types.Types function in flytekit

def test_sdk_launch_plan_node():
    @_tasks.inputs(a=_types.Types.Integer)
    @_tasks.outputs(b=_types.Types.Integer)
    @_tasks.python_task()
    def testy_test(wf_params, a, b):
        pass

    @_workflow.workflow_class
    class test_workflow(object):
        a = _workflow.Input(_types.Types.Integer)
        test = testy_test(a=1)
        b = _workflow.Output(test.outputs.b, sdk_type=_types.Types.Integer)

    lp = test_workflow.create_launch_plan()

    lp._id = _identifier.Identifier(_identifier.ResourceType.TASK, 'project', 'domain', 'name', 'version')
    n = _component_nodes.SdkWorkflowNode(sdk_launch_plan=lp)
    assert n.launchplan_ref.project == 'project'
    assert n.launchplan_ref.domain == 'domain'
    assert n.launchplan_ref.name == 'name'
    assert n.launchplan_ref.version == 'version'

    # Test floating ID
    lp._id = _identifier.Identifier(
        _identifier.ResourceType.TASK,
        'new_project',

    @_sdk_tasks.inputs(a=_Types.Integer)
    @_sdk_tasks.outputs(b=_Types.Integer, c=_Types.Integer)
    @_sdk_tasks.python_task()
    def demo_task_for_promote(wf_params, a, b, c):
        b.set(a + 1)
        c.set(a + 2)

from __future__ import absolute_import

from flytekit.sdk.tasks import inputs
from flytekit.sdk.types import Types
from flytekit.sdk.workflow import workflow_class, Input, Output
from flytekit.common.tasks.presto_task import SdkPrestoTask

schema = Types.Schema([("a", Types.String), ("b", Types.Integer)])

presto_task = SdkPrestoTask(
    task_inputs=inputs(ds=Types.String, rg=Types.String),
    statement="SELECT * FROM hive.city.fact_airport_sessions WHERE ds = '{{ .Inputs.ds}}' LIMIT 10",
    output_schema=schema,
    routing_group="{{ .Inputs.rg }}",
    # catalog="hive",
    # schema="city",
)


@workflow_class()
class PrestoWorkflow(object):
    ds = Input(Types.String, required=True, help="Test string with no default")
    # routing_group = Input(Types.String, required=True, help="Test string with no default")

    @inputs(num=Types.Integer)
    @outputs(out=Types.Integer)
    @python_task
    def inner_task(wf_params, num, out):
        wf_params.logging.info("Running inner task... setting output to input")
        out.set(num)

    @outputs(out1=Types.String)
    @sidecar_task(
        cpu_request='10',
        gpu_limit='2',
        environment={"foo": "bar"},
        pod_spec=get_pod_spec(),
        primary_container_name="a container",
    )
    def simple_sidecar_task(wf_params, in1, out1):
        pass

from __future__ import absolute_import
from flytekit.common import constants
from flytekit.common.tasks import sdk_runnable
from flytekit.common.types import helpers
from flytekit.models import interface
from flytekit.sdk import types
import six


GOOD_INPUTS = {
    'a': types.Types.Integer,
    'name': types.Types.String,
}
GOOD_OUTPUTS = {
    'x': types.Types.Integer,
}
GOOD_NOTEBOOK = sdk_runnable.RunnableNotebookTask(
    notebook_path="notebooks/good.ipynb",
    inputs={
        k: interface.Variable(
            helpers.python_std_to_sdk_type(v).to_flyte_literal_type(),
            ''
        )
        for k, v in six.iteritems(GOOD_INPUTS)
    },
    outputs={
        k: interface.Variable(

def test_raw_container_task_definition_no_outputs():
    tk = SdkRawContainerTask(
        inputs={"x": Types.Integer},
        image="my-image",
        command=["echo", "hello, world!"],
        gpu_limit="1",
        gpu_request="1",
    )
    assert not tk.serialize() is None
    task_instance = tk(x=3)
    assert task_instance.inputs[0].binding.scalar.primitive.integer == 3

@_tasks.inputs(num=_Types.Integer)
@_tasks.outputs(out=_Types.Integer)
@_tasks.python_task
def inner_task(wf_params, num, out):
    wf_params.logging.info("Running inner task... setting output to input")
    out.set(num)

def test_simple_training_job_task():
    assert isinstance(simple_training_job_task, SdkSimpleTrainingJobTask)
    assert isinstance(simple_training_job_task, _sdk_task.SdkTask)
    assert simple_training_job_task.interface.inputs['train'].description == ''
    assert simple_training_job_task.interface.inputs['train'].type == \
        _sdk_types.Types.MultiPartCSV.to_flyte_literal_type()
    assert simple_training_job_task.interface.inputs['validation'].description == ''
    assert simple_training_job_task.interface.inputs['validation'].type == \
        _sdk_types.Types.MultiPartCSV.to_flyte_literal_type()
    assert simple_training_job_task.interface.inputs['static_hyperparameters'].description == ''
    assert simple_training_job_task.interface.inputs['static_hyperparameters'].type == \
        _sdk_types.Types.Generic.to_flyte_literal_type()
    assert simple_training_job_task.interface.inputs['stopping_condition'].type == \
        _sdk_types.Types.Proto(_pb2_StoppingCondition).to_flyte_literal_type()
    assert simple_training_job_task.interface.outputs['model'].description == ''
    assert simple_training_job_task.interface.outputs['model'].type == \
        _sdk_types.Types.Blob.to_flyte_literal_type()
    assert simple_training_job_task.type == _common_constants.SdkTaskType.SAGEMAKER_TRAINING_JOB_TASK
    assert simple_training_job_task.metadata.timeout == _datetime.timedelta(seconds=0)
    assert simple_training_job_task.metadata.deprecated_error_message == ''
    assert simple_training_job_task.metadata.discoverable is False
    assert simple_training_job_task.metadata.discovery_version == ''

dimensionality=_core_types.BlobType.BlobDimensionality.MULTIPART
                            ),
                        ),
                        description="",
                    ),
                    "validation": _interface_model.Variable(
                        type=_idl_types.LiteralType(
                            blob=_core_types.BlobType(
                                format="csv",
                                dimensionality=_core_types.BlobType.BlobDimensionality.MULTIPART
                            ),
                        ),
                        description="",
                    ),
                    "stopping_condition": _interface_model.Variable(
                        _sdk_types.Types.Proto(_training_job_pb2.StoppingCondition).to_flyte_literal_type(), ""
                    )
                },
                outputs={
                    "model": _interface_model.Variable(
                        type=_idl_types.LiteralType(
                            blob=_core_types.BlobType(
                                format="",
                                dimensionality=_core_types.BlobType.BlobDimensionality.SINGLE
                            )
                        ),
                        description=""
                    )
                }
            ),
            custom=MessageToDict(self._training_job_model.to_flyte_idl()),
        )