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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def nansem(values, axis=None, skipna=True, ddof=1, mask=None):
 """ 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 -------- 0.5773502691896258 """


    # This checks if non-numeric-like data is passed with numeric_only=False
    # and raises a TypeError otherwise
    nanvar(values, axis, skipna, ddof=ddof, mask=mask)

    if mask is None:
        mask = isna(values)
    if not is_float_dtype(values.dtype):
        values = values.astype('f8')
    count, _ = _get_counts_nanvar(mask, axis, ddof, values.dtype)
    var = nanvar(values, axis, skipna, ddof=ddof)

    return np.sqrt(var) / np.sqrt(count)