class_docstr: Reports verbose output to stdout.
  
  Parameters
  ----------
  verbose : int
      Verbosity level. If ``verbose==1`` output is printed once in a while
      (when iteration mod verbose_mod is zero).; if larger than 1 then output
      is printed for each update.
class_docstr_tok: ["Reports","verbose","output","to","stdout",".","Parameters","--","--","--","--","--","verbose",":","int","Verbosity","level",".","If","``","verbose==1","``","output","is","printed","once","in","a","while","(","when","iteration","mod","verbose_mod","is","zero",")",".",";","if","larger","than","1","then","output","is","printed","for","each","update","."]
code: def __init__(self, verbose):
      self.verbose = verbose
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from numbers import Integral, Real
  from time import time
  import numpy
  from scipy.sparse import csc_matrix, csr_matrix, issparse
  from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import NotFittedError
  from ..model_selection import train_test_split
  from ..tree import DecisionTreeRegressor
  from ..tree._tree import DOUBLE, DTYPE
  from ..utils import check_array, check_random_state, column_or_1d
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _check_sample_weight, check_is_fitted
  from .None import _gb_losses
  from ._base import BaseEnsemble
  from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages
label_desc: Use image node_id 1 to create a new VerboseReporter object  with example: obj = VerboseReporter(verbose)