class_docstr: xDeepFM model
  
  :Citation:
  
      J. Lian, X. Zhou, F. Zhang, Z. Chen, X. Xie, G. Sun, "xDeepFM: Combining Explicit
      and Implicit Feature Interactions for Recommender Systems", in Proceedings of the
      24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining,
      KDD 2018, London, 2018.
docstr: Construct the linear part for the model.
  This is a linear regression.
  
  Returns:
      object: Prediction score made by linear regression.
code: def _build_linear(self):
      """Construct the linear part for the model.
      This is a linear regression.
  
      Returns:
          object: Prediction score made by linear regression.
      """
      with tf.compat.v1.variable_scope(
          "linear_part", initializer=self.initializer
      ) as scope:  # noqa: F841
          w = tf.compat.v1.get_variable(
              name="w",
              shape=[self.hparams.FEATURE_COUNT, 1],
              dtype=tf.float32,
          )
          b = tf.compat.v1.get_variable(
              name="b",
              shape=[1],
              dtype=tf.float32,
              initializer=tf.compat.v1.zeros_initializer(),
          )
          x = tf.SparseTensor(
              self.iterator.fm_feat_indices,
              self.iterator.fm_feat_values,
              self.iterator.fm_feat_shape,
          )
          linear_output = tf.add(tf.sparse.sparse_dense_matmul(x, w), b)
          self.layer_params.append(w)
          self.layer_params.append(b)
          tf.compat.v1.summary.histogram("linear_part/w", w)
          tf.compat.v1.summary.histogram("linear_part/b", b)
          return linear_output
  
file_path: recommenders/recommenders/models/deeprec/models/xDeepFM.py
imports: import numpy
  import tensorflow
  from recommenders.models.deeprec.models.base_model import BaseModel
label_desc: Use image node_id 3 for calling the XDeepFMModel obj's underlying member method code with example usage: obj._build_linear() and returns: linear_output