code: def sample_from_parallelepiped(low, high, n_samples, random_state):
      (n_features,) = low.shape
      X = np.zeros((n_samples, n_features))
      for i in range(n_features):
          X[:, i] = random_state.uniform(low[i], high[i], n_samples)
      return X
  
imports: import numpy
  from numpy.testing import assert_allclose, assert_array_less
  import pytest
  from sklearn.utils import check_random_state
  from ..hmm import GMMHMM
  from .test_gmm_hmm import create_random_gmm
  from .None import assert_log_likelihood_increasing, normalized
label_desc: Use image node_id 1 for calling a global function with example usage: sample_from_parallelepiped(low, high, n_samples, random_state) and returns: X