Examples

Index > MachineLearning > Examples

Auto-generated documentation for MachineLearning type annotations stubs module types-boto3-machinelearning.

Client

Implicit type annotations

Can be used with types-boto3[machinelearning] package installed.

Write your MachineLearning code as usual, type checking and code completion should work out of the box.

Client method usage example

# MachineLearningClient usage example

from boto3.session import Session


session = Session()

client = session.client("machinelearning")  # (1)
result = client.add_tags()  # (2)

1. client: MachineLearningClient
2. result: AddTagsOutputTypeDef

Paginator usage example

# DescribeBatchPredictionsPaginator usage example

from boto3.session import Session


session = Session()
client = session.client("machinelearning")  # (1)

paginator = client.get_paginator("describe_batch_predictions")  # (2)
for item in paginator.paginate(...):
    print(item)  # (3)

1. client: MachineLearningClient
2. paginator: DescribeBatchPredictionsPaginator
3. item: DescribeBatchPredictionsOutputTypeDef

Waiter usage example

# BatchPredictionAvailableWaiter usage example

from boto3.session import Session


session = Session()
client = session.client("machinelearning")  # (1)

waiter = client.get_waiter("batch_prediction_available")  # (2)
waiter.wait(...)

1. client: MachineLearningClient
2. waiter: BatchPredictionAvailableWaiter

Explicit type annotations

With types-boto3-lite[machinelearning] or a standalone types_boto3_machinelearning package, you have to explicitly specify client: MachineLearningClient type annotation.

All other type annotations are optional, as types should be discovered automatically. However, these type annotations can be helpful in your functions and methods.

Client method usage example

# MachineLearningClient usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.type_defs import AddTagsOutputTypeDef
from types_boto3_machinelearning.type_defs import AddTagsInputTypeDef


session = Session()

client: MachineLearningClient = session.client("machinelearning")

kwargs: AddTagsInputTypeDef = {...}
result: AddTagsOutputTypeDef = client.add_tags(**kwargs)

Paginator usage example

# DescribeBatchPredictionsPaginator usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.paginator import DescribeBatchPredictionsPaginator
from types_boto3_machinelearning.type_defs import DescribeBatchPredictionsOutputTypeDef


session = Session()
client: MachineLearningClient = session.client("machinelearning")

paginator: DescribeBatchPredictionsPaginator = client.get_paginator("describe_batch_predictions")
for item in paginator.paginate(...):
    item: DescribeBatchPredictionsOutputTypeDef
    print(item)

Waiter usage example

# BatchPredictionAvailableWaiter usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.waiter import BatchPredictionAvailableWaiter

session = Session()
client: MachineLearningClient = session.client("machinelearning")

waiter: BatchPredictionAvailableWaiter = client.get_waiter("batch_prediction_available")
waiter.wait(...)