mypy-boto3-frauddetector type stubs

Warnings

• breaking `mypy-boto3-builder` 8.12.0 and later versions removed support for Python 3.8 across all generated packages. This package (1.42.3) requires Python 3.9 or newer.
  Fix: Upgrade your Python environment to version 3.9 or later.
• breaking Starting with `mypy-boto3-builder` 8.9.0, TypedDict names for method arguments may have changed (e.g., `CreateDistributionRequestRequestTypeDef` -> `CreateDistributionRequestTypeDef`). Additionally, conflicting `Extra` postfixes were moved (`CreateDistributionExtraRequestTypeDef` -> `CreateDistributionRequestExtraTypeDef`).
  Fix: Update your TypedDict imports and usage to reflect the new naming conventions, consulting the service's documentation if necessary.
• gotcha When using `if TYPE_CHECKING:` to prevent runtime stub imports, Pylint might report 'undefined variable' errors for the type-hinted objects.
  Fix: To fix this, assign `object` to the type-hinted variables in the `else` branch: `if TYPE_CHECKING: from mypy_boto3_frauddetector import FraudDetectorClient else: FraudDetectorClient = object`.
• gotcha PyCharm has known performance issues with `Literal` overloads (issue PY-40997) when using full `mypy-boto3-*` packages.
  Fix: If experiencing performance problems in PyCharm, consider installing the `boto3-stubs-lite` version (e.g., `pip install 'boto3-stubs-lite[frauddetector]'`) which is more RAM-friendly but requires explicit type annotations for session clients/resources.
• gotcha While `boto3-stubs` often auto-discovers types for `session.client()` or `boto3.client()`, explicit type annotations for the client object (e.g., `client: FraudDetectorClient = ...`) are highly recommended for optimal IDE auto-completion and strict type checking, especially with `boto3-stubs-lite` variants.
  Fix: Always add explicit type annotations for `boto3` client and resource objects.

Install

• pip install boto3 mypy-boto3-frauddetector Install with boto3

Imports

• FraudDetectorClient

  from mypy_boto3_frauddetector import FraudDetectorClient

• AsyncJobStatusType

  from mypy_boto3_frauddetector.literals import AsyncJobStatusType

  For literal types like enum values.
• ATIMetricDataPointTypeDef

  from mypy_boto3_frauddetector.type_defs import ATIMetricDataPointTypeDef

  For TypedDicts representing service request/response structures.

Quickstart

This example demonstrates how to initialize a FraudDetector client with explicit type annotations and make a simple API call (`get_event_types`). The `TYPE_CHECKING` guard ensures the type stub package is only imported for static analysis, avoiding a runtime dependency. Replace `DUMMY_KEY` and `DUMMY_SECRET` with actual credentials or ensure standard AWS credential providers are configured.

import os
from typing import TYPE_CHECKING
from boto3.session import Session

# Only import type stubs during type checking
if TYPE_CHECKING:
    from mypy_boto3_frauddetector import FraudDetectorClient
    from mypy_boto3_frauddetector.type_defs import GetEventTypesResultTypeDef

# Instantiate a boto3 session (credentials are handled by boto3's default chain)
session = Session(
    aws_access_key_id=os.environ.get('AWS_ACCESS_KEY_ID', 'DUMMY_KEY'),
    aws_secret_access_key=os.environ.get('AWS_SECRET_ACCESS_KEY', 'DUMMY_SECRET'),
    region_name=os.environ.get('AWS_DEFAULT_REGION', 'us-east-1')
)

# Get the FraudDetector client with type annotations
client: 'FraudDetectorClient' = session.client("frauddetector")

try:
    # Example API call with type-hinted response
    response: 'GetEventTypesResultTypeDef' = client.get_event_types()
    print("Successfully retrieved FraudDetector event types.")
    for event_type in response.get('EventTypes', []):
        print(f" - Event Type: {event_type.get('Name')}")
except Exception as e:
    print(f"Error retrieving FraudDetector event types: {e}")