class LambdaContext defined in /var/runtime/awslambda/bootstrap.py which is used to launch users functions and has the following structure:

class LambdaContext(object):
    def __init__(self, invokeid, context_objs, client_context, invoked_function_arn=None):
        self.aws_request_id = invokeid
        self.log_group_name = os.environ['AWS_LAMBDA_LOG_GROUP_NAME']
        self.log_stream_name = os.environ['AWS_LAMBDA_LOG_STREAM_NAME']
        self.function_name = os.environ["AWS_LAMBDA_FUNCTION_NAME"]
        self.memory_limit_in_mb = os.environ['AWS_LAMBDA_FUNCTION_MEMORY_SIZE']
        self.function_version = os.environ['AWS_LAMBDA_FUNCTION_VERSION']
        self.invoked_function_arn = invoked_function_arn

        self.client_context = make_obj_from_dict(ClientContext, client_context)
        if self.client_context is not None:
            self.client_context.client = make_obj_from_dict(Client, self.client_context.client)
        self.identity = make_obj_from_dict(CognitoIdentity, context_objs)

    def get_remaining_time_in_millis(self):
        return lambda_runtime.get_remaining_time()

    def log(self, msg):
        str_msg = str(msg)
        lambda_runtime.send_console_message(str_msg, byte_len(str_msg))

If you want to emulate it on your local environment, just add it into your script:

class ClientContext(object):
    __slots__ = ['custom', 'env', 'client']


def make_obj_from_dict(_class, _dict, fields=None):
    if _dict is None:
        return None
    obj = _class()
    set_obj_from_dict(obj, _dict)
    return obj


def set_obj_from_dict(obj, _dict, fields=None):
    if fields is None:
        fields = obj.__class__.__slots__
    for field in fields:
        setattr(obj, field, _dict.get(field, None))


class LambdaContext(object):
    def __init__(self, invokeid, context_objs, client_context, invoked_function_arn=None):
        self.aws_request_id = invokeid
        self.log_group_name = os.environ['AWS_LAMBDA_LOG_GROUP_NAME']
        self.log_stream_name = os.environ['AWS_LAMBDA_LOG_STREAM_NAME']
        self.function_name = os.environ["AWS_LAMBDA_FUNCTION_NAME"]
        self.memory_limit_in_mb = os.environ['AWS_LAMBDA_FUNCTION_MEMORY_SIZE']
        self.function_version = os.environ['AWS_LAMBDA_FUNCTION_VERSION']
        self.invoked_function_arn = invoked_function_arn

        self.client_context = make_obj_from_dict(ClientContext, client_context)
        if self.client_context is not None:
            self.client_context.client = None
        self.identity = None


    def get_remaining_time_in_millis(self):
        return None

    def log(self, msg):
        str_msg = str(msg)
        print(str_msg)
        # lambda_runtime.send_console_message(str_msg, byte_len(str_msg))

One possible workaround for getting lambdas working with a context manager is to make the context manager a ContextDecorator, then both with statements and lambda expressions will work because a lambda can use the decorator pattern instead.

Example

from contextlib import ContextDecorator


def f(x):
     """Just prints the input, but this could be any arbitrary function."""
     print(x)


class mycontext(ContextDecorator):
    def __enter__(self):
        f('Starting')
        return self

    def __exit__(self, *exc):
        f('Finishing')
        return False

with mycontext():
    f('The bit in the middle')

mycontext()(lambda: f('The bit in the middle'))()

Unfortunately, the above didn't work for me. Got around this using the following:

from dataclasses import dataclass

@pytest.fixture
def context():
    @dataclass
    class LambdaContext:
        function_name: str = "test"
        aws_request_id: str = "88888888-4444-4444-4444-121212121212"
        invoked_function_arn: str = "arn:aws:lambda:eu-west-1:123456789101:function:test"


    return LambdaContext()

def test_lambda(context):
    my_class = MyClass(EVENT, context)