code: def _estimate_rank_raw(
      raw,
      picks=None,
      tol=1e-4,
      scalings="norm",
      with_ref_meg=False,
      tol_kind="absolute",
  ):
      """Aid the transition away from raw.estimate_rank."""
      if picks is None:
          picks = _picks_to_idx(
              raw.info, picks, with_ref_meg=with_ref_meg
          )
      # conveniency wrapper to expose the expert "tol" option + scalings options
      return _estimate_rank_meeg_signals(
          raw[picks][0],
          pick_info(raw.info, picks),
          scalings,
          tol,
          False,
          tol_kind,
      )
  
file_path: mne-python/mne/rank.py
imports: import numpy
  from scipy import linalg
  from ._fiff.meas_info import Info, _simplify_info
  from ._fiff.pick import _picks_by_type, _picks_to_idx, pick_channels_cov, pick_info
  from ._fiff.proj import make_projector
  from .defaults import _handle_default
  from .utils import _apply_scaling_cov, _check_on_missing, _check_rank, _compute_row_norms, _on_missing, _pl, _scaled_array, _undo_scaling_cov, _validate_type, fill_doc, logger, verbose, warn
label_desc: Use image node_id 3 for calling a global function with example usage: _estimate_rank_raw(raw, picks, tol, scalings, with_ref_meg, tol_kind) and returns: _estimate_rank_meeg_signals