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: Initialize reporter
  
  Parameters
  ----------
  est : Estimator
      The estimator
  
  begin_at_stage : int, default=0
      stage at which to begin reporting
code: def init(self, est, begin_at_stage=0):
      """Initialize reporter
  
      Parameters
      ----------
      est : Estimator
          The estimator
  
      begin_at_stage : int, default=0
          stage at which to begin reporting
      """
      # header fields and line format str
      header_fields = ["Iter", "Train Loss"]
      verbose_fmt = ["{iter:>10d}", "{train_score:>16.4f}"]
      # do oob?
      if est.subsample < 1:
          header_fields.append("OOB Improve")
          verbose_fmt.append("{oob_impr:>16.4f}")
      header_fields.append("Remaining Time")
      verbose_fmt.append("{remaining_time:>16s}")
  
      # print the header line
      print(
          ("%10s " + "%16s " * (len(header_fields) - 1))
          % tuple(header_fields)
      )
  
      self.verbose_fmt = " ".join(verbose_fmt)
      # plot verbose info each time i % verbose_mod == 0
      self.verbose_mod = 1
      self.start_time = time()
      self.begin_at_stage = begin_at_stage
  
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 2 for calling the VerboseReporter obj's underlying member method code with example usage: obj.init(est, begin_at_stage) without return types