code: def nansem(
      values: np.ndarray,
      *,
      axis: AxisInt | None = None,
      skipna: bool = True,
      ddof: int = 1,
      mask: npt.NDArray[np.bool_] | None = None,
  ) -> float:
      """
      Compute the standard error in the mean along given axis while ignoring NaNs
  
      Parameters
      ----------
      values : ndarray
      axis : int, optional
      skipna : bool, default True
      ddof : int, default 1
          Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
          where N represents the number of elements.
      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, 2, 3])
      >>> nanops.nansem(s)
       0.5773502691896258
      """
      # This checks if non-numeric-like data is passed with numeric_only=False
      # and raises a TypeError otherwise
      nanvar(values, axis=axis, skipna=skipna, ddof=ddof, mask=mask)
  
      mask = _maybe_get_mask(values, skipna, mask)
      if not is_float_dtype(values.dtype):
          values = values.astype("f8")
  
      if not skipna and mask is not None and mask.any():
          return np.nan
  
      count, _ = _get_counts_nanvar(
          values.shape, mask, axis, ddof, values.dtype
      )
      var = nanvar(
          values, axis=axis, skipna=skipna, ddof=ddof, mask=mask
      )
  
      return np.sqrt(var) / np.sqrt(count)
  
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 22 for calling a global function with example usage: nansem(values) and returns: unknown, np