code: def _uniform_bins(timestamp_min, timestamp_max, n_timestamps, n_bins):
      bin_edges = np.empty((n_timestamps, n_bins - 1))
      for i in prange(n_timestamps):
          bin_edges[i] = np.linspace(
              timestamp_min[i], timestamp_max[i], n_bins + 1
          )[1:-1]
      return bin_edges
  
imports: import numpy
  from numba import njit, prange
  from scipy.stats import norm
  from sklearn.base import BaseEstimator
  from ..base import UnivariateTransformerMixin
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
  from sklearn.utils.multiclass import check_classification_targets
label_desc: Use image node_id 1 for calling a global function with example usage: _uniform_bins(timestamp_min, timestamp_max, n_timestamps, n_bins) and returns: bin_edges