code: def __init__(
      self, use_cross_layer, deep_layer_sizes, projection_dim=None
  ):
      super().__init__()
  
      self.embedding_dimension = 32
  
      str_features = ["movie_id", "user_id", "zip_code", "occupation"]
      int_features = ["gender", "age"]
  
      self._all_features = str_features + int_features
      self._embeddings = {}
  
      # Compute embeddings for string features.
      for feature_name in str_features:
          vocabulary = vocabularies[feature_name]
          self._embeddings[feature_name] = tf.keras.Sequential(
              [
                  tf.keras.layers.StringLookup(
                      vocabulary=vocabulary, mask_token=None
                  ),
                  tf.keras.layers.Embedding(
                      len(vocabulary) + 1, self.embedding_dimension
                  ),
              ]
          )
  
      # Compute embeddings for int features.
      for feature_name in int_features:
          vocabulary = vocabularies[feature_name]
          self._embeddings[feature_name] = tf.keras.Sequential(
              [
                  tf.keras.layers.IntegerLookup(
                      vocabulary=vocabulary, mask_value=None
                  ),
                  tf.keras.layers.Embedding(
                      len(vocabulary) + 1, self.embedding_dimension
                  ),
              ]
          )
  
      if use_cross_layer:
          self._cross_layer = tfrs.layers.dcn.Cross(
              projection_dim=projection_dim,
              kernel_initializer="glorot_uniform",
          )
      else:
          self._cross_layer = None
  
      self._deep_layers = [
          tf.keras.layers.Dense(layer_size, activation="relu")
          for layer_size in deep_layer_sizes
      ]
  
      self._logit_layer = tf.keras.layers.Dense(1)
  
      self.task = tfrs.tasks.Ranking(
          loss=tf.keras.losses.MeanSquaredError(),
          metrics=[tf.keras.metrics.RootMeanSquaredError("RMSE")],
      )
  
file_path: BigDL/python/orca/example/learn/tf2/dcn/dcn_parquet.py
imports: import sys
  from optparse import OptionParser
  import math
  import tensorflow
  import tensorflow_recommenders
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context
  from bigdl.orca.learn.tf2.estimator import Estimator
  from bigdl.orca.data.tf.data import Dataset
  from bigdl.friesian.feature import FeatureTable
label_desc: Use image node_id 1 to create a new DCN object from inherited base classes: tfrs with example: obj = DCN(use_cross_layer, deep_layer_sizes, projection_dim)