class_docstr: Wide and deep model, implemented using TFLearn
docstr: Load data (use files offered in the Tensorflow wide_n_deep_tutorial)
docstr_tok: ["Load","data","(","use","files","offered","in","the","Tensorflow","wide_n_deep_tutorial",")"]
code: def load_data(self, train_dfn="adult.data", test_dfn="adult.test"):
      """
      Load data (use files offered in the Tensorflow wide_n_deep_tutorial)
      """
      if not os.path.exists(train_dfn):
          urllib.urlretrieve(
              "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
              train_dfn,
          )
          print("Training data is downloaded to %s" % train_dfn)
  
      if not os.path.exists(test_dfn):
          urllib.urlretrieve(
              "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test",
              test_dfn,
          )
          print("Test data is downloaded to %s" % test_dfn)
  
      self.train_data = pd.read_csv(
          train_dfn, names=COLUMNS, skipinitialspace=True
      )
      self.test_data = pd.read_csv(
          test_dfn, names=COLUMNS, skipinitialspace=True, skiprows=1
      )
  
      self.train_data[self.label_column] = (
          self.train_data["income_bracket"].apply(lambda x: ">50K" in x)
      ).astype(int)
      self.test_data[self.label_column] = (
          self.test_data["income_bracket"].apply(lambda x: ">50K" in x)
      ).astype(int)
  
imports: from __future__ import division, print_function
  import os
  import sys
  import argparse
  import tflearn
  import tempfile
  import urllib
  import numpy
  import pandas
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the TFLearnWideAndDeep obj's underlying member method code with example usage: obj.load_data(train_dfn, test_dfn) without return types