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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)
  1. client: MachineLearningClient
  2. 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)
  1. client: MachineLearningClient
  2. paginator: DescribeBatchPredictionsPaginator
  3. 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()
  1. client: MachineLearningClient
  2. 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)
  1. client: MachineLearningClient
  2. kwargs: AddTagsInputRequestTypeDef
  3. 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)
  1. client: MachineLearningClient
  2. paginator: DescribeBatchPredictionsPaginator
  3. 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()
  1. client: MachineLearningClient
  2. waiter: BatchPredictionAvailableWaiter