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","."]
docstr: Update reporter with new iteration.
  
  Parameters
  ----------
  j : int
      The new iteration.
  est : Estimator
      The estimator.
code: def update(self, j, est):
      """Update reporter with new iteration.
  
      Parameters
      ----------
      j : int
          The new iteration.
      est : Estimator
          The estimator.
      """
      do_oob = est.subsample < 1
      # we need to take into account if we fit additional estimators.
      i = (
          j - self.begin_at_stage
      )  # iteration relative to the start iter
      if (i + 1) % self.verbose_mod == 0:
          oob_impr = est.oob_improvement_[j] if do_oob else 0
          remaining_time = (
              (est.n_estimators - (j + 1))
              * (time() - self.start_time)
              / float(i + 1)
          )
          if remaining_time > 60:
              remaining_time = "{0:.2f}m".format(remaining_time / 60.0)
          else:
              remaining_time = "{0:.2f}s".format(remaining_time)
          print(
              self.verbose_fmt.format(
                  iter=j + 1,
                  train_score=est.train_score_[j],
                  oob_impr=oob_impr,
                  remaining_time=remaining_time,
              )
          )
          if self.verbose == 1 and (
              (i + 1) // (self.verbose_mod * 10) > 0
          ):
              # adjust verbose frequency (powers of 10)
              self.verbose_mod *= 10
  
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 3 for calling the VerboseReporter obj's underlying member method code with example usage: obj.update(j, est) without return types