Ensures newline (\n) denotation renders correctly.

Author: jamie-lemon

docs/page.rst

@@ -504,7 +504,7 @@ In a nutshell, this is what you can do with PyMuPDF:
            * ``bbox``: the bounding box of the table as a tuple `(x0, y0, x1, y1)`.
            * ``cells``: bounding boxes of the table's cells (list of tuples). A cell may also be `None`.
            * ``extract()``: this method returns the text content of each table cell as a list of list of strings.
-           * ``to_markdown()``: this method returns the table as a **string in markdown format** (compatible to Github). Markdown viewers can render the string as a table. This output is optimized for **small token** sizes, which is especially beneficial for LLM/RAG feeds. Pandas DataFrames (see method `to_pandas()` below) offer an equivalent markdown table output which however is better readable for the human eye. Any line breaks (`\n`) in cells are replaced by HTML line breaks tags `<br>`.
+           * ``to_markdown()``: this method returns the table as a **string in markdown format** (compatible to Github). Markdown viewers can render the string as a table. This output is optimized for **small token** sizes, which is especially beneficial for LLM/RAG feeds. Pandas DataFrames (see method `to_pandas()` below) offer an equivalent markdown table output which however is better readable for the human eye. Any line breaks (``\n``) in cells are replaced by HTML line breaks tags `<br>`.
            * `to_pandas()`: this method returns the table as a `pandas <https://pypi.org/project/pandas/>`_ `DataFrame <https://pandas.pydata.org/docs/reference/frame.html>`_. DataFrames are very versatile objects allowing a plethora of table manipulation methods and outputs to almost 20 well-known formats, among them Excel files, CSV, JSON, markdown-formatted tables and more. `DataFrame.to_markdown()` generates a Github-compatible markdown format optimized for human readability. This method however requires the package `tabulate <https://pypi.org/project/tabulate/>`_ to be installed in addition to pandas itself.
            * ``header``: a `TableHeader` object containing header information of the table.
            * ``col_count``: an integer containing the number of table columns.