Examples
Index > MachineLearning > Examples
Auto-generated documentation for MachineLearning type annotations stubs module types-aiobotocore-machinelearning.
Client
Implicit type annotations
Can be used with types-aioboto3[machinelearning]
package installed.
Write your MachineLearning
code as usual,
type checking and code completion should work out of the box.
Client usage example
from aioboto3.session import Session
session = Session()
async with session.client("machinelearning") as client: # (1)
result = await client.add_tags() # (2)
- client: MachineLearningClient
- result: AddTagsOutputTypeDef
Paginator usage example
from aioboto3.session import Session
session = Session()
async with session.client("machinelearning") as client: # (1)
paginator = client.get_paginator("describe_batch_predictions") # (2)
async for item in paginator.paginate(...):
print(item) # (3)
- client: MachineLearningClient
- paginator: DescribeBatchPredictionsPaginator
- item: DescribeBatchPredictionsOutputTypeDef
Waiter usage example
from aioboto3.session import Session
session = Session()
async with session.client("machinelearning") as client: # (1)
waiter = client.get_waiter("batch_prediction_available") # (2)
await waiter.wait()
- client: MachineLearningClient
- waiter: BatchPredictionAvailableWaiter
Explicit type annotations
With types-aioboto3-lite[machinelearning]
or a standalone types_aiobotocore_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 usage example
from aioboto3.session import Session
from types_aiobotocore_machinelearning.client import MachineLearningClient
from types_aiobotocore_machinelearning.type_defs import AddTagsOutputTypeDef
from types_aiobotocore_machinelearning.type_defs import AddTagsInputRequestTypeDef
session = Session()
client: MachineLearningClient
async with session.client("machinelearning") as client: # (1)
kwargs: AddTagsInputRequestTypeDef = {...} # (2)
result: AddTagsOutputTypeDef = await client.add_tags(**kwargs) # (3)
- client: MachineLearningClient
- kwargs: AddTagsInputRequestTypeDef
- result: AddTagsOutputTypeDef
Paginator usage example
from aioboto3.session import Session
from types_aiobotocore_machinelearning.client import MachineLearningClient
from types_aiobotocore_machinelearning.paginator import DescribeBatchPredictionsPaginator
from types_aiobotocore_machinelearning.type_defs import DescribeBatchPredictionsOutputTypeDef
session = Session()
client: MachineLearningClient
async with session.client("machinelearning") as client: # (1)
paginator: DescribeBatchPredictionsPaginator = client.get_paginator("describe_batch_predictions") # (2)
async for item in paginator.paginate(...):
item: DescribeBatchPredictionsOutputTypeDef
print(item) # (3)
- client: MachineLearningClient
- paginator: DescribeBatchPredictionsPaginator
- item: DescribeBatchPredictionsOutputTypeDef
Waiter usage example
from aioboto3.session import Session
from types_aiobotocore_machinelearning.client import MachineLearningClient
from types_aiobotocore_machinelearning.waiter import BatchPredictionAvailableWaiter
session = Session()
async with session.client("machinelearning") as client: # (1)
waiter = client.get_waiter("batch_prediction_available") # (2)
await waiter.wait()
- client: MachineLearningClient
- waiter: BatchPredictionAvailableWaiter