code: def _make_evokeds(coefs, conds, cond_length, tmin_s, tmax_s, info):
      """Create a dictionary of Evoked objects.
  
      These will be created from a coefs matrix and condition durations.
      """
      evokeds = dict()
      cumul = 0
      for cond in conds:
          tmin_, tmax_ = tmin_s[cond], tmax_s[cond]
          evokeds[cond] = EvokedArray(
              coefs[:, cumul : cumul + tmax_ - tmin_],
              info=info,
              comment=cond,
              tmin=tmin_ / float(info["sfreq"]),
              nave=cond_length[cond],
              kind="average",
          )  # nave and kind are technically incorrect
          cumul += tmax_ - tmin_
      return evokeds
  
file_path: mne-python/mne/stats/regression.py
imports: from collections import namedtuple
  from inspect import isgenerator
  import numpy
  from scipy import linalg, sparse, stats
  from .._fiff.pick import _picks_to_idx, pick_info, pick_types
  from ..epochs import BaseEpochs
  from ..evoked import Evoked, EvokedArray
  from ..source_estimate import SourceEstimate
  from ..utils import _reject_data_segments, fill_doc, logger, warn
label_desc: Use image node_id 7 for calling a global function with example usage: _make_evokeds(coefs, conds, cond_length, tmin_s, tmax_s, info) and returns: evokeds