class_docstr: Base class for sequential models
class_docstr_tok: ["Base","class","for","sequential","models"]
docstr: Lookup from embedding variables. A dropout layer follows lookup operations.
docstr_tok: ["Lookup","from","embedding","variables",".","A","dropout","layer","follows","lookup","operations","."]
code: def _lookup_from_embedding(self):
      """Lookup from embedding variables. A dropout layer follows lookup operations."""
      self.user_embedding = tf.nn.embedding_lookup(
          params=self.user_lookup, ids=self.iterator.users
      )
      tf.compat.v1.summary.histogram(
          "user_embedding_output", self.user_embedding
      )
  
      self.item_embedding = tf.compat.v1.nn.embedding_lookup(
          params=self.item_lookup, ids=self.iterator.items
      )
      self.item_history_embedding = tf.compat.v1.nn.embedding_lookup(
          params=self.item_lookup, ids=self.iterator.item_history
      )
      tf.compat.v1.summary.histogram(
          "item_history_embedding_output", self.item_history_embedding
      )
  
      self.cate_embedding = tf.compat.v1.nn.embedding_lookup(
          params=self.cate_lookup, ids=self.iterator.cates
      )
      self.cate_history_embedding = tf.compat.v1.nn.embedding_lookup(
          params=self.cate_lookup, ids=self.iterator.item_cate_history
      )
      tf.compat.v1.summary.histogram(
          "cate_history_embedding_output", self.cate_history_embedding
      )
  
      involved_items = tf.concat(
          [
              tf.reshape(self.iterator.item_history, [-1]),
              tf.reshape(self.iterator.items, [-1]),
          ],
          -1,
      )
      self.involved_items, _ = tf.unique(involved_items)
      involved_item_embedding = tf.nn.embedding_lookup(
          params=self.item_lookup, ids=self.involved_items
      )
      self.embed_params.append(involved_item_embedding)
  
      involved_cates = tf.concat(
          [
              tf.reshape(self.iterator.item_cate_history, [-1]),
              tf.reshape(self.iterator.cates, [-1]),
          ],
          -1,
      )
      self.involved_cates, _ = tf.unique(involved_cates)
      involved_cate_embedding = tf.nn.embedding_lookup(
          params=self.cate_lookup, ids=self.involved_cates
      )
      self.embed_params.append(involved_cate_embedding)
  
      self.target_item_embedding = tf.concat(
          [self.item_embedding, self.cate_embedding], -1
      )
      tf.compat.v1.summary.histogram(
          "target_item_embedding_output", self.target_item_embedding
      )
  
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 8 for calling the SequentialBaseModel obj's underlying member method code with example usage: obj._lookup_from_embedding() without return types