code: def _construct_signal_from_epochs(epochs, events, sfreq, tmin):
      """Reconstruct pseudo continuous signal from epochs."""
      n_epochs, n_channels, n_times = epochs.shape
      tmax = tmin + n_times / float(sfreq)
      start = np.min(events[:, 0]) + int(tmin * sfreq)
      stop = np.max(events[:, 0]) + int(tmax * sfreq) + 1
  
      n_samples = stop - start
      n_epochs, n_channels, n_times = epochs.shape
      events_pos = events[:, 0] - events[0, 0]
  
      raw = np.zeros((n_channels, n_samples))
      for idx in range(n_epochs):
          onset = events_pos[idx]
          offset = onset + n_times
          raw[:, onset:offset] = epochs[idx]
  
      return raw
  
imports: import numpy
  from scipy import linalg
  from .._fiff.pick import _pick_data_channels, pick_info
  from ..cov import Covariance, _regularized_covariance
  from ..decoding import BaseEstimator, TransformerMixin
  from ..epochs import BaseEpochs
  from ..evoked import Evoked, EvokedArray
  from ..io import BaseRaw
  from ..utils import _check_option, logger
label_desc: Use image node_id 1 for calling a global function with example usage: _construct_signal_from_epochs(epochs, events, sfreq, tmin) and returns: raw