class_docstr: [WIP] Random Forest Regressor.
  
      
class_docstr_tok: ["[","WIP","]","Random","Forest","Regressor","."]
code: def __init__(
      self,
      n_estimators=10,
      max_nodes=100,
      split_after_samples=25,
      n_features=None,
      num_output=None,
      metric=None,
      log_dir="/tmp/tflearn_logs/",
      global_step=None,
      session=None,
      graph=None,
      name=None,
  ):
      super(RandomForestRegressor, self).__init__(
          n_estimators=n_estimators,
          max_nodes=max_nodes,
          split_after_samples=split_after_samples,
          regression=True,
          n_classes=num_output,
          n_features=n_features,
          metric=metric,
          log_dir=log_dir,
          global_step=global_step,
          session=session,
          graph=graph,
          name=name,
      )
  
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 RandomForestRegressor object from inherited base classes: ForestEstimator with example: obj = RandomForestRegressor(n_estimators, max_nodes, split_after_samples, n_features, num_output, metric, log_dir, global_step, session, graph, name)