code: def read_fine_calibration(fname):
      """Read fine calibration information from a ``.dat`` file.
  
      The fine calibration typically includes improved sensor locations,
      calibration coefficients, and gradiometer imbalance information.
  
      Parameters
      ----------
      fname : path-like
          The filename.
  
      Returns
      -------
      calibration : dict
          Fine calibration information. Key-value pairs are:
  
          - ``ch_names``
               List of str of the channel names.
          - ``locs``
               Coil location and orientation parameters.
          - ``imb_cals``
               For magnetometers, the calibration coefficients.
               For gradiometers, one or three imbalance parameters.
      """
      # Read new sensor locations
      fname = _check_fname(fname, overwrite="read", must_exist=True)
      check_fname(fname, "cal", (".dat",))
      ch_names, locs, imb_cals = list(), list(), list()
      with open(fname) as fid:
          for line in fid:
              if line[0] in "#\n":
                  continue
              vals = line.strip().split()
              if len(vals) not in [14, 16]:
                  raise RuntimeError(
                      "Error parsing fine calibration file, "
                      "should have 14 or 16 entries per line "
                      "but found %s on line:\n%s" % (len(vals), line)
                  )
              # `vals` contains channel number
              ch_name = vals[0]
              if len(ch_name) in (3, 4):  # heuristic for Neuromag fix
                  try:
                      ch_name = int(ch_name)
                  except (
                      ValueError
                  ):  # something other than e.g. 113 or 2642
                      pass
                  else:
                      ch_name = "MEG" + "%04d" % ch_name
              # (x, y, z), x-norm 3-vec, y-norm 3-vec, z-norm 3-vec
              # and 1 or 3 imbalance terms
              ch_names.append(ch_name)
              locs.append(np.array(vals[1:13], float))
              imb_cals.append(np.array(vals[13:], float))
      locs = np.array(locs)
      return dict(ch_names=ch_names, locs=locs, imb_cals=imb_cals)
  
imports: from collections import defaultdict
  from functools import partial
  import numpy
  from scipy.optimize import fmin_cobyla
  from .._fiff.pick import pick_info, pick_types
  from .._fiff.tag import _coil_trans_to_loc, _loc_to_coil_trans
  from ..bem import _check_origin
  from ..io import BaseRaw
  from ..transforms import _find_vector_rotation
  from ..utils import _check_fname, _check_option, _ensure_int, _pl, _reg_pinv, _validate_type, check_fname, logger, verbose
  from .maxwell import _col_norm_pinv, _get_grad_point_coilsets, _prep_fine_cal, _prep_mf_coils, _read_cross_talk, _trans_sss_basis
label_desc: Use image node_id 10 for calling a global function with example usage: read_fine_calibration(fname) and returns: dict