bug: Incorrect transformation for Markdown list

[nasaul]

Description of the bug

For this docstring it transforms the following:

    Algorithm:
    1.   For all series compute conditional marginals distributions.
    2.   Compute residuals $\hat{\epsilon}_{i,t}$ and obtain rank permutations.
    3.   Obtain K-sample from the bottom-level series predictions.
    4.   Apply recursively through the hierarchical structure:
        4.1.   For a given aggregate series $i$ and its children series:
        4.2.   Obtain children's empirical joint using sample reordering copula.
        4.2.   From the children's joint obtain the aggregate series's samples.

Into:

Algorithm:

1. For all series compute conditional marginals distributions.
1. Compute residuals $\hat{\epsilon}_{i,t}$ and obtain rank permutations.
1. Obtain K-sample from the bottom-level series predictions.
1. Apply recursively through the hierarchical structure:

4.1.   For a given aggregate series $i$ and its children series:
4.2.   Obtain children's empirical joint using sample reordering copula.
4.2.   From the children's joint obtain the aggregate series's samples.

To Reproduce

hierarchicalforecast = griffe.load("hierarchicalforecast")

config = ConfigDict(
    heading_level=3,
    show_root_heading=True,
    show_source=True,
    docstring_section_style="table",
    summary={"functions": False},
)

print(
    render_object_docs(
        hierarchicalforecast["probabilistic_methods.PERMBU"],
        config,
    )
)

Full traceback

Full traceback

PASTE TRACEBACK HERE

Expected behavior

The numbers shouldn't change in the list.

Environment information

- __System__: macOS-15.4.1-arm64-arm-64bit
- __Python__: cpython 3.10.19 (/Users/nasaul/nixtla/hierarchicalforecast/.venv/bin/python3)
- __Environment variables__:
- __Installed packages__:
  - `griffe2md` v1.2.6

Additional context

@deven367

[pawamoy]

Thanks for the report @nasaul.

I'm not sure this is valid Markdown. Can you try with:

    Algorithm:
    1.   For all series compute conditional marginals distributions.
    2.   Compute residuals $\hat{\epsilon}_{i,t}$ and obtain rank permutations.
    3.   Obtain K-sample from the bottom-level series predictions.
    4.   Apply recursively through the hierarchical structure:
        1.   For a given aggregate series $i$ and its children series:
        2.   Obtain children's empirical joint using sample reordering copula.
        3.   From the children's joint obtain the aggregate series's samples.

In any case, you might need to disable formatting with mdformat here.