def METHOD_NAME(
    self,
    tmp_path: Path,
    fxt_login: Tuple[Client, str],
):
    self.client = fxt_login[0]
    self.images = generate_image_files(10)
    image_dir = tmp_path / "images"
    image_dir.mkdir()
    image_paths = []
    for image in self.images:
        image_path = image_dir / image.name
        image_path.write_bytes(image.getbuffer())
        image_paths.append(image_path)
    self.task = self.client.tasks.create_from_data(
        models.TaskWriteRequest(
            "Dataset layer test task",
            labels=[
                models.PatchedLabelRequest(name="person"),
                models.PatchedLabelRequest(name="car"),
            ],
        ),
        resource_type=ResourceType.LOCAL,
        resources=image_paths,
        data_params={"chunk_size": 3},
    )
    self.expected_labels = sorted(self.task.get_labels(), key=lambda l: l.id)
    self.task.update_annotations(
        models.PatchedLabeledDataRequest(
            tags=[
                models.LabeledImageRequest(frame=8, label_id=self.expected_labels[0].id),
                models.LabeledImageRequest(frame=8, label_id=self.expected_labels[1].id),
            ],
            shapes=[
                models.LabeledShapeRequest(
                    frame=6,
                    label_id=self.expected_labels[1].id,
                    type=models.ShapeType("rectangle"),
                    points=[1.0, 2.0, 3.0, 4.0],
                ),
            ],
        )
    )