class_docstr: Base class to process rendering a Styler with a specified jinja2 template.
class_docstr_tok: ["Base","class","to","process","rendering","a","Styler","with","a","specified","jinja2","template","."]
docstr: Renders the ``Styler`` including all applied styles to HTML.
  Generates a dict with necessary kwargs passed to jinja2 template.
docstr_tok: ["Renders","the","``","Styler","``","including","all","applied","styles","to","HTML",".","Generates","a","dict","with","necessary","kwargs","passed","to","jinja2","template","."]
code: def _render_html(
      self,
      sparse_index: bool,
      sparse_columns: bool,
      max_rows: int | None = None,
      max_cols: int | None = None,
      **kwargs,
  ) -> str:
      """
      Renders the ``Styler`` including all applied styles to HTML.
      Generates a dict with necessary kwargs passed to jinja2 template.
      """
      d = self._render(
          sparse_index, sparse_columns, max_rows, max_cols, "&nbsp;"
      )
      d.update(kwargs)
      return self.template_html.render(
          **d,
          html_table_tpl=self.template_html_table,
          html_style_tpl=self.template_html_style,
      )
  
imports: from __future__ import annotations
  from collections import defaultdict
  from collections.abc import Sequence
  from functools import partial
  import re
  from typing import TYPE_CHECKING, Any, Callable, DefaultDict, Optional, TypedDict, Union
  from uuid import uuid4
  import numpy
  from pandas._config import get_option
  from pandas._libs import lib
  from pandas.compat._optional import import_optional_dependency
  from pandas.core.dtypes.common import is_complex, is_float, is_integer
  from pandas.core.dtypes.generic import ABCSeries
  from pandas import DataFrame, Index, IndexSlice, MultiIndex, Series, isna
  from pandas.api.types import is_list_like
  import pandas.core.common
  from markupsafe import escape
label_desc: Use image node_id 3 for calling the StylerRenderer obj's underlying member method code with example usage: obj._render_html(sparse_index, sparse_columns, max_rows, max_cols) and returns: self