class_docstr: Base class for sequential models
class_docstr_tok: ["Base","class","for","sequential","models"]
docstr: The field embedding layer. Initialization of embedding variables.
docstr_tok: ["The","field","embedding","layer",".","Initialization","of","embedding","variables","."]
code: def _build_embedding(self):
      """The field embedding layer. Initialization of embedding variables."""
      hparams = self.hparams
      self.user_vocab_length = len(load_dict(hparams.user_vocab))
      self.item_vocab_length = len(load_dict(hparams.item_vocab))
      self.cate_vocab_length = len(load_dict(hparams.cate_vocab))
      self.user_embedding_dim = hparams.user_embedding_dim
      self.item_embedding_dim = hparams.item_embedding_dim
      self.cate_embedding_dim = hparams.cate_embedding_dim
  
      with tf.compat.v1.variable_scope(
          "embedding", initializer=self.initializer
      ):
          self.user_lookup = tf.compat.v1.get_variable(
              name="user_embedding",
              shape=[self.user_vocab_length, self.user_embedding_dim],
              dtype=tf.float32,
          )
          self.item_lookup = tf.compat.v1.get_variable(
              name="item_embedding",
              shape=[self.item_vocab_length, self.item_embedding_dim],
              dtype=tf.float32,
          )
          self.cate_lookup = tf.compat.v1.get_variable(
              name="cate_embedding",
              shape=[self.cate_vocab_length, self.cate_embedding_dim],
              dtype=tf.float32,
          )
  
file_path: recommenders/recommenders/models/deeprec/models/sequential/sequential_base_model.py
imports: import os
  import abc
  import numpy
  import tensorflow
  from recommenders.models.deeprec.models.base_model import BaseModel
  from recommenders.models.deeprec.deeprec_utils import cal_metric, load_dict
label_desc: Use image node_id 7 for calling the SequentialBaseModel obj's underlying member method code with example usage: obj._build_embedding() without return types