code: def _uniform_bins(sample_min, sample_max, n_samples, n_bins):
      bin_edges = np.empty((n_bins - 1, n_samples))
      for i in prange(n_samples):
          bin_edges[:, i] = np.linspace(
              sample_min[i], sample_max[i], n_bins + 1
          )[1:-1]
      return bin_edges
  
imports: import numpy
  from numba import njit, prange
  from numba.typed import List
  from scipy.stats import norm
  from sklearn.base import BaseEstimator
  from sklearn.utils.validation import check_array
  from warnings import warn
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 1 for calling a global function with example usage: _uniform_bins(sample_min, sample_max, n_samples, n_bins) and returns: bin_edges