code: def __init__(
      self,
      wide_column=None,
      deep_column=None,
      dnn_hidden_units=[1024, 512, 256],
      optimizer_type="adam",
      linear_learning_rate=0.2,
      deep_learning_rate=0.01,
      inputs=None,
      bf16=False,
      stock_tf=None,
      adaptive_emb=False,
      input_layer_partitioner=None,
      dense_layer_partitioner=None,
  ):
      if not inputs:
          invalidInputError(False, "Dataset is not defined.")
      self._feature = inputs[0]
      self._label = inputs[1]
  
      self._wide_column = wide_column
      self._deep_column = deep_column
      if not wide_column or not deep_column:
          invalidInputError(
              False, "Wide column or Deep column is not defined."
          )
  
      self.tf = stock_tf
      self.bf16 = False if self.tf else bf16
      self.is_training = True
      self._adaptive_emb = adaptive_emb
  
      self._dnn_hidden_units = dnn_hidden_units
      self._linear_learning_rate = linear_learning_rate
      self._deep_learning_rate = deep_learning_rate
      self._optimizer_type = optimizer_type
      self._input_layer_partitioner = input_layer_partitioner
      self._dense_layer_partitioner = dense_layer_partitioner
  
      self._create_model()
      with tf.name_scope("head"):
          self._create_loss()
          self._create_optimizer()
          self._create_metrics()
  
imports: import time
  import argparse
  import tensorflow
  import os
  import sys
  import math
  import collections
  from tensorflow.python.client import timeline
  import json
  from tensorflow.python.ops import partitioned_variables
  import ray
  from bigdl.dllib.utils.log4Error import *
  from bigdl.orca import init_orca_context, stop_orca_context, OrcaContext
  from bigdl.orca.data.utils import process_spark_xshards
  from bigdl.orca.learn.utils import maybe_dataframe_to_xshards
  from bigdl.friesian.feature import FeatureTable
label_desc: Use image node_id 1 to create a new WDL object  with example: obj = WDL(wide_column, deep_column, dnn_hidden_units, optimizer_type, linear_learning_rate, deep_learning_rate, inputs, bf16, stock_tf, adaptive_emb, input_layer_partitioner, dense_layer_partitioner)