I have a setup where there is a node.js application that writes to a kinesis stream using putRecords for two items. The kinesis stream feeds a Lambda function that is configured with an eventSourceMapping with a batch size of 1. However, when I debug what the Records Kinesis feeds the lambda for an invocation, I see that the lambda is processing two records. Both of these records are on the same shard and have a sequence number which is pretty close.

The interesting thing is that if I switch the node.js published to use putRecord instead of putRecords, the kinesis records have a slightly farther apart sequence number and the lambda is fed the records individually.

The java code to configure the event source mapping
```
        AWSLambdaAsync lambdaClient = AWSResourceFactory.getLambdaClient();
        CreateEventSourceMappingRequest request = new CreateEventSourceMappingRequest();
        request.setEventSourceArn(kinesisArn);
        request.setFunctionName(functionName);
        request.setEnabled(true);
        request.setStartingPosition(EventSourcePosition.TRIM_HORIZON);
        request.setBatchSize(1);
        lambdaClient.createEventSourceMapping(request);
```

The localstack lambda output that shows multiple records being read at a time:

```
{
	"Records": [{
		"eventID": "shardId-000000000000:49593014281924840790139112603493819506073986345293840386",
		"eventSourceARN": "arn:aws:kinesis:us-east-1:000000000000:stream/MyStream",
		"kinesis": {
			"partitionKey": "ed0f756b97234f4fae9e3d1a886b8fbc",
			"data": "myData1",
			"sequenceNumber": "49593014281924840790139112603493819506073986345293840386"
		}
	}, {
		"eventID": "shardId-000000000000:49593014281924840790139112603495028431893600974468546562",
		"eventSourceARN": "arn:aws:kinesis:us-east-1:000000000000:stream/MyStream",
		"kinesis": {
			"partitionKey": "abee63a9299385699d65e89a290a9065",
			"data": "myData2",
			"sequenceNumber": "49593014281924840790139112603495028431893600974468546562"
		}
	}]
}
```

def __init__(self, msg, code=400):
        super(ClientError, self).__init__(msg)
        self.code = code
        self.msg = msg
    def get_response(self):
        if isinstance(self.msg, Response):
            return self.msg
        return error_response(self.msg, self.code)
class LambdaContext(object):
    def __init__(self, func_details, qualifier=None):
        self.function_name = func_details.name()
        self.function_version = func_details.get_qualifier_version(qualifier)
        self.invoked_function_arn = func_details.arn()
        if qualifier:
            self.invoked_function_arn += ':' + qualifier
    def get_remaining_time_in_millis(self):
        
        return 1000 * 60
def cleanup():
    global event_source_mappings, arn_to_lambda
    arn_to_lambda = {}
    event_source_mappings = []
    LAMBDA_EXECUTOR.cleanup()
def func_arn(function_name):
    return aws_stack.lambda_function_arn(function_name)
def add_function_mapping(lambda_name, lambda_handler, lambda_cwd=None):
    arn = func_arn(lambda_name)
    arn_to_lambda[arn].versions.get('$LATEST')['Function'] = lambda_handler
    arn_to_lambda[arn].cwd = lambda_cwd
def add_event_source(function_name, source_arn, enabled):