class_docstr: [WIP] ForesEstimator
      
class_docstr_tok: ["[","WIP","]","ForesEstimator"]
code: def __init__(
      self,
      n_estimators=100,
      max_nodes=10000,
      split_after_samples=25,
      min_samples_split=2,
      bagging_fraction=1.0,
      num_splits_to_consider=0,
      feature_bagging_fraction=1.0,
      max_fertile_nodes=0,
      valid_leaf_threshold=1,
      dominate_method="bootstrap",
      dominate_fraction=0.99,
      regression=False,
      n_classes=None,
      n_features=None,
      metric=None,
      log_dir="/tmp/tflearn_logs/",
      global_step=None,
      session=None,
      graph=None,
      name=None,
  ):
      super(ForestEstimator, self).__init__(
          metric=metric,
          log_dir=log_dir,
          global_step=global_step,
          session=session,
          graph=graph,
          name=name,
      )
      self._estimator_built = False
  
      # Tree Params
      self.n_estimators = n_estimators
      self.max_nodes = max_nodes
      self.split_after_samples = split_after_samples
      self.min_samples_split = min_samples_split
      self.regression = regression
      self.n_classes = n_classes
      self.n_features = n_features
      self.bagging_fraction = bagging_fraction
      self.num_splits_to_consider = num_splits_to_consider
      self.feature_bagging_fraction = feature_bagging_fraction
      self.max_fertile_nodes = max_fertile_nodes
      self.valid_leaf_threshold = valid_leaf_threshold
      self.dominate_method = dominate_method
      self.dominate_fraction = dominate_fraction
  
imports: from __future__ import division, print_function, absolute_import
  from datetime import datetime
  import os
  import math
  import numpy
  import time
  import tensorflow.compat.v1
  from tensorflow.contrib.tensor_forest.python import tensor_forest
  from tensorflow.contrib.tensor_forest.python.ops import data_ops
  from tensorflow.python.ops import state_ops, array_ops, math_ops
  from ...utils import validate_dim, read_tensor_in_checkpoint
  from ...data_utils import get_num_features, get_num_classes, get_num_sample
  from ...data_flow import generate_data_tensor
  from ..base import BaseEstimator
label_desc: Use image node_id 1 to create a new ForestEstimator object from inherited base classes: BaseEstimator with example: obj = ForestEstimator(n_estimators, max_nodes, split_after_samples, min_samples_split, bagging_fraction, num_splits_to_consider, feature_bagging_fraction, max_fertile_nodes, valid_leaf_threshold, dominate_method, dominate_fraction, regression, n_classes, n_features, metric, log_dir, global_step, session, graph, name)