code: def nankurt(
      values: np.ndarray,
      *,
      axis: AxisInt | None = None,
      skipna: bool = True,
      mask: npt.NDArray[np.bool_] | None = None,
  ) -> float:
      """
      Compute the sample excess kurtosis
  
      The statistic computed here is the adjusted Fisher-Pearson standardized
      moment coefficient G2, computed directly from the second and fourth
      central moment.
  
      Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      mask : ndarray[bool], optional
          nan-mask if known
  
      Returns
      -------
      result : float64
          Unless input is a float array, in which case use the same
          precision as the input array.
  
      Examples
      --------
      >>> from pandas.core import nanops
      >>> s = pd.Series([1, np.nan, 1, 3, 2])
      >>> nanops.nankurt(s)
      -1.2892561983471076
      """
      # error: Incompatible types in assignment (expression has type "Union[Any,
      # Union[ExtensionArray, ndarray]]", variable has type "ndarray")
      values = extract_array(values, extract_numpy=True)  # type: ignore[assignment]
      mask = _maybe_get_mask(values, skipna, mask)
      if not is_float_dtype(values.dtype):
          values = values.astype("f8")
          count = _get_counts(values.shape, mask, axis)
      else:
          count = _get_counts(
              values.shape, mask, axis, dtype=values.dtype
          )
  
      if skipna and mask is not None:
          values = values.copy()
          np.putmask(values, mask, 0)
      elif not skipna and mask is not None and mask.any():
          return np.nan
  
      mean = values.sum(axis, dtype=np.float64) / count
      if axis is not None:
          mean = np.expand_dims(mean, axis)
  
      adjusted = values - mean
      if skipna and mask is not None:
          np.putmask(adjusted, mask, 0)
      adjusted2 = adjusted**2
      adjusted4 = adjusted2**2
      m2 = adjusted2.sum(axis, dtype=np.float64)
      m4 = adjusted4.sum(axis, dtype=np.float64)
  
      with np.errstate(invalid="ignore", divide="ignore"):
          adj = 3 * (count - 1) ** 2 / ((count - 2) * (count - 3))
          numerator = count * (count + 1) * (count - 1) * m4
          denominator = (count - 2) * (count - 3) * m2**2
  
      # floating point error
      #
      # #18044 in _libs/windows.pyx calc_kurt follow this behavior
      # to fix the fperr to treat denom <1e-14 as zero
      numerator = _zero_out_fperr(numerator)
      denominator = _zero_out_fperr(denominator)
  
      if not isinstance(denominator, np.ndarray):
          # if ``denom`` is a scalar, check these corner cases first before
          # doing division
          if count < 4:
              return np.nan
          if denominator == 0:
              return 0
  
      with np.errstate(invalid="ignore", divide="ignore"):
          result = numerator / denominator - adj
  
      dtype = values.dtype
      if is_float_dtype(dtype):
          result = result.astype(dtype, copy=False)
  
      if isinstance(result, np.ndarray):
          result = np.where(denominator == 0, 0, result)
          result[count < 4] = np.nan
  
      return result
  
imports: from __future__ import annotations
  import functools
  import itertools
  import operator
  from typing import Any, Callable, cast
  import warnings
  import numpy
  from pandas._config import get_option
  from pandas._libs import NaT, NaTType, iNaT, lib
  from pandas._typing import ArrayLike, AxisInt, CorrelationMethod, Dtype, DtypeObj, F, Scalar, Shape, npt
  from pandas.compat._optional import import_optional_dependency
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_any_int_dtype, is_bool_dtype, is_complex, is_datetime64_any_dtype, is_float, is_float_dtype, is_integer, is_integer_dtype, is_numeric_dtype, is_object_dtype, is_scalar, is_timedelta64_dtype, needs_i8_conversion, pandas_dtype
  from pandas.core.dtypes.dtypes import PeriodDtype
  from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
  from pandas.core.construction import extract_array
label_desc: Use image node_id 27 for calling a global function with example usage: nankurt(values) and returns: result, np, np, int