code: def iqr(a, c=Gaussian.ppf(3 / 4) - Gaussian.ppf(1 / 4), axis=0):
      """
      The normalized interquartile range along given axis of an array
  
      Parameters
      ----------
      a : array_like
          Input array.
      c : float, optional
          The normalization constant, used to get consistent estimates of the
          standard deviation at the normal distribution.  Defined as
          scipy.stats.norm.ppf(3/4.) - scipy.stats.norm.ppf(1/4.), which is
          approximately 1.349.
      axis : int, optional
          The default is 0. Can also be None.
  
      Returns
      -------
      The normalized interquartile range
      """
      a = array_like(a, "a", ndim=None)
      c = float_like(c, "c")
  
      if a.ndim == 0:
          raise ValueError("a should have at least one dimension")
      elif a.size == 0:
          return np.nan
      else:
          quantiles = np.quantile(a, [0.25, 0.75], axis=axis)
          return np.squeeze(np.diff(quantiles, axis=0) / c)
  
file_path: statsmodels/statsmodels/robust/scale.py
imports: import numpy
  from scipy.stats import norm
  from statsmodels.tools import tools
  from statsmodels.tools.validation import array_like, float_like
  from .None import norms
  from ._qn import _qn
label_desc: Use image node_id 2 for calling a global function with example usage: iqr(a, c, axis) and returns: np, np