class_docstr: Generate uniformly distributed points in the log10 space.
class_docstr_tok: ["Generate","uniformly","distributed","points","in","the","log10","space","."]
code: def __init__(self, min_val, max_val):
      self.min_val = min_val
      self.max_val = max_val
      self.gen = uniform(0, 1)
  
imports: import inspect
  import numpy
  from abc import ABC
  from operator import itemgetter
  from collections import namedtuple
  from sklearn.model_selection import StratifiedKFold
  from tick.base import Base
  from tick.prox import ProxZero, ProxMulti, ProxTV, ProxEquality, ProxGroupL1
  from tick.solver import SVRG
  from tick.survival import SimuSCCS, ModelSCCS
  from tick.preprocessing import LongitudinalSamplesFilter, LongitudinalFeaturesLagger
  from scipy.stats import uniform
  import matplotlib.pylab
  from mpl_toolkits.mplot3d import Axes3D
  from matplotlib import cm
  from tick.preprocessing.utils import check_longitudinal_features_consistency, check_censoring_consistency, safe_array
label_desc: Use image node_id 1 to create a new Log10UniformGenerator object  with example: obj = Log10UniformGenerator(min_val, max_val)