class_docstr: Store information about each estimator.
  
  Parameters
  ----------
  algorithm : str
      Abbreviation representing the given algorithm.
  model_type : enum ModelType
      The machine learning task for this algorithm.
  estimator : function
      A scikit-learn, TensorFlow, or XGBoost function.
  grid : dict
      The dictionary of hyperparameters for grid search.
class_docstr_tok: ["Store","information","about","each","estimator",".","Parameters","--","--","--","--","--","algorithm",":","str","Abbreviation","representing","the","given","algorithm",".","model_type",":","enum","ModelType","The","machine","learning","task","for","this","algorithm",".","estimator",":","function","A","scikit-learn",",","TensorFlow",",","or","XGBoost","function",".","grid",":","dict","The","dictionary","of","hyperparameters","for","grid","search","."]
code: def __init__(self, algorithm, model_type, estimator, grid):
      self.algorithm = algorithm.upper()
      self.model_type = model_type
      self.estimator = estimator
      self.grid = grid
  
imports: from alphapy.globals import ModelType
  from alphapy.globals import Objective
  from alphapy.globals import SSEP
  from keras.layers import *
  from keras.models import Sequential
  from keras.wrappers.scikit_learn import KerasClassifier
  from keras.wrappers.scikit_learn import KerasRegressor
  import logging
  import numpy
  from scipy.stats import randint
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.ensemble import ExtraTreesClassifier
  from sklearn.ensemble import ExtraTreesRegressor
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import LogisticRegression
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neighbors import KNeighborsRegressor
  from sklearn.svm import LinearSVC
  from sklearn.svm import SVC
  import sys
  import yaml
label_desc: Use image node_id 2 to create a new Estimator object  with example: obj = Estimator(algorithm, model_type, estimator, grid)