code: def fit_iir_model_raw(
      raw, order=2, picks=None, tmin=None, tmax=None, verbose=None
  ):
      r"""Fit an AR model to raw data and creates the corresponding IIR filter.
  
      The computed filter is fitted to data from all of the picked channels,
      with frequency response given by the standard IIR formula:
  
      .. math::
  
          H(e^{jw}) = \frac{1}{a[0] + a[1]e^{-jw} + ... + a[n]e^{-jnw}}
  
      Parameters
      ----------
      raw : Raw object
          An instance of Raw.
      order : int
          Order of the FIR filter.
      %(picks_good_data)s
      tmin : float
          The beginning of time interval in seconds.
      tmax : float
          The end of time interval in seconds.
      %(verbose)s
  
      Returns
      -------
      b : ndarray
          Numerator filter coefficients.
      a : ndarray
          Denominator filter coefficients.
      """
      start, stop = None, None
      if tmin is not None:
          start = raw.time_as_index(tmin)[0]
      if tmax is not None:
          stop = raw.time_as_index(tmax)[0] + 1
      picks = _picks_to_idx(raw.info, picks)
      data = raw[picks, start:stop][0]
      # rescale data to similar levels
      picks_list = _picks_by_type(pick_info(raw.info, picks))
      scalings = _handle_default("scalings_cov_rank", None)
      _apply_scaling_array(
          data, picks_list=picks_list, scalings=scalings
      )
      # do the fitting
      coeffs, _ = _yule_walker(data, order=order)
      return np.array([1.0]), np.concatenate(([1.0], -coeffs))
  
imports: import numpy
  from scipy import linalg
  from .._fiff.pick import _picks_by_type, _picks_to_idx, pick_info
  from ..defaults import _handle_default
  from ..utils import _apply_scaling_array, verbose
label_desc: Use image node_id 2 for calling a global function with example usage: fit_iir_model_raw(raw, order, picks, tmin, tmax, verbose) and returns: np,  np