code: def _compute_cov_epochs(epochs, n_jobs):
      """Compute epochs covariance."""
      parallel, p_fun, n_jobs = parallel_func(np.dot, n_jobs)
      data = parallel(p_fun(e, e.T) for e in epochs)
      n_epochs = len(data)
      if n_epochs == 0:
          raise RuntimeError("No good epochs found")
  
      n_chan, n_samples = epochs.info["nchan"], len(epochs.times)
      _check_n_samples(n_samples * n_epochs, n_chan)
      data = sum(data)
      return data
  
imports: import numpy
  from ._fiff.constants import FIFF
  from ._fiff.open import fiff_open
  from ._fiff.pick import _picks_to_idx, pick_types, pick_types_forward
  from ._fiff.proj import Projection, _has_eeg_average_ref_proj, _read_proj, _write_proj, make_eeg_average_ref_proj, make_projector
  from ._fiff.write import start_and_end_file
  from .cov import _check_n_samples
  from .epochs import Epochs
  from .event import make_fixed_length_events
  from .fixes import _safe_svd
  from .forward import _subject_from_forward, convert_forward_solution, is_fixed_orient
  from .parallel import parallel_func
  from .source_estimate import _make_stc
  from .utils import _check_fname, _check_option, _validate_type, check_fname, logger, verbose
label_desc: Use image node_id 5 for calling a global function with example usage: _compute_cov_epochs(epochs, n_jobs) and returns: data