How to use the papermill.__version__ function in papermill

def test_02_notebook_run(detection_notebooks, tiny_od_mask_data_path):
    notebook_path = detection_notebooks["02"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(
            PM_VERSION=pm.__version__,
            DATA_PATH=tiny_od_mask_data_path,
            EPOCHS=1,
        ),
        kernel_name=KERNEL_NAME,
    )
    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
    assert len(nb_output.scraps["training_losses"].data) > 0
    training_aps = nb_output.scraps["training_average_precision"].data
    assert len(training_aps) > 0
    for d in training_aps:
        assert isinstance(d, dict)
    assert len(set([len(d) for d in training_aps])) == 1

def test_od_20_notebook_run(
    detection_notebooks,
    subscription_id,
    resource_group,
    workspace_name,
    workspace_region,
):
    notebook_path = detection_notebooks["20_deployment_on_kubernetes"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(
            PM_VERSION=pm.__version__,
            subscription_id=subscription_id,
            resource_group=resource_group,
            workspace_name=workspace_name,
            workspace_region=workspace_region,
        ),
        kernel_name=KERNEL_NAME,
    )

def test_10_notebook_run(classification_notebooks, tiny_ic_data_path):
    notebook_path = classification_notebooks["10_image_annotation"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(
            PM_VERSION=pm.__version__,
            IM_DIR=os.path.join(tiny_ic_data_path, "can"),
        ),
        kernel_name=KERNEL_NAME,
    )

    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
    assert nb_output.scraps["num_images"].data == 6

def test_01_notebook_run(classification_notebooks):
    notebook_path = classification_notebooks["01_training_introduction"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(PM_VERSION=pm.__version__),
        kernel_name=KERNEL_NAME,
    )

    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
    assert len(nb_output.scraps["training_accuracies"].data) == 10
    assert nb_output.scraps["training_accuracies"].data[-1] > 0.70
    assert nb_output.scraps["validation_accuracy"].data > 0.70

def test_12_notebook_run(detection_notebooks):
    notebook_path = detection_notebooks["12"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(PM_VERSION=pm.__version__, EPOCHS=3),
        kernel_name=KERNEL_NAME,
    )

    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
    assert nb_output.scraps["valid_accs"].data[-1] > 0.5
    assert len(nb_output.scraps["valid_accs"].data) == 1
    assert len(nb_output.scraps["hard_im_scores"].data) == 10

def test_11_notebook_run(similarity_notebooks, tiny_ic_data_path):
    notebook_path = similarity_notebooks["11"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(
            PM_VERSION=pm.__version__,
            # Speed up testing since otherwise would take ~12 minutes on V100
            DATA_PATHS=[tiny_ic_data_path],
            REPS=1,
            IM_SIZES=[60, 100],
        ),
        kernel_name=KERNEL_NAME,
    )
    nb_output = sb.read_notebook(OUTPUT_NOTEBOOK)
    assert min(nb_output.scraps["ranks"].data) <= 30

def test_01_notebook_run(similarity_notebooks, tiny_ic_data_path):
    notebook_path = similarity_notebooks["01"]
    pm.execute_notebook(
        notebook_path,
        OUTPUT_NOTEBOOK,
        parameters=dict(
            PM_VERSION=pm.__version__,
            DATA_PATH=tiny_ic_data_path,
            EPOCHS_HEAD=1,
            EPOCHS_BODY=1,
            IM_SIZE=50,
        ),
        kernel_name=KERNEL_NAME,
    )

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = 'papermill'
copyright = '2018, nteract team'
author = 'nteract team'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
import papermill

# The short X.Y version.
version = '.'.join(papermill.__version__.split('.')[0:2])

# The full version, including alpha/beta/rc tags.
release = papermill.__version__

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line foexitr these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'UPDATE.md']

# General information about the project.
project = 'papermill'
copyright = '2018, nteract team'
author = 'nteract team'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
import papermill

# The short X.Y version.
version = '.'.join(papermill.__version__.split('.')[0:2])

# The full version, including alpha/beta/rc tags.
release = papermill.__version__

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line foexitr these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'UPDATE.md']

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

"""Returns a notebook object with papermill metadata loaded from the specified path.

    Args:
        notebook_path (str): Path to the notebook file.

    Returns:
        nbformat.NotebookNode

    """
    nb = nbformat.reads(papermill_io.read(notebook_path), as_version=4)

    if not hasattr(nb.metadata, 'papermill'):
        nb.metadata['papermill'] = {
            'parameters': dict(),
            'environment_variables': dict(),
            'version': __version__,
        }

    for cell in nb.cells:
        if not hasattr(cell.metadata, 'tags'):
            cell.metadata['tags'] = []  # Create tags attr if one doesn't exist.

        if not hasattr(cell.metadata, 'papermill'):
            cell.metadata['papermill'] = dict()
    return nb