class_docstr: [WIP] ForesEstimator
      
class_docstr_tok: ["[","WIP","]","ForesEstimator"]
docstr: predict.
  
  Predict scores for X.
  
  Arguments:
      X: `1-D Array` or `2-D Array` of shape (n_samples, n_features).
          The sample(s) to predict.
  
  Return:
      `Array` or `list` of `Array`. Prediction scores result.
docstr_tok: ["predict",".","Predict","scores","for","X",".","Arguments",":","X",":","`","1-D","Array","`","or","`","2-D","Array","`","of","shape","(","n_samples",",","n_features",")",".","The","sample","(","s",")","to","predict",".","Return",":","`","Array","`","or","`","list","`","of","`","Array","`",".","Prediction","scores","result","."]
code: def predict(self, X):
      """predict.
  
      Predict scores for X.
  
      Arguments:
          X: `1-D Array` or `2-D Array` of shape (n_samples, n_features).
              The sample(s) to predict.
  
      Return:
          `Array` or `list` of `Array`. Prediction scores result.
  
      """
      with self.graph.as_default():
          # Build Tree Graph
          self._build_estimator()
          if not self._pred.is_ready:
              input = tf.placeholder(
                  tf.float32,
                  name="pred_input",
                  shape=[None, self.n_features],
              )
              output, _, _ = self.forest_graph.inference_graph(input)
              self._build_pred(input, output)
          return self.session.run(
              self._pred.output_tensor,
              feed_dict={self._pred.input_tensor: X},
          )
  
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 4 for calling the ForestEstimator obj's underlying member method code with example usage: obj.predict(X) and returns: self