parameters: (self, inputs, training=True)
return_statement: return emb_vector
docstring: The forward logic of this wrapper class.
  
          Parameters
          ----------
          inputs: tf.Tensor
                  keys are stored in tf.Tensor. It must be stored in row-major.
                  If `dynamic_input = True`, then inputs.shape must be [None,], 
                  otherwise, inputs.shape must be [batchsize, slot_num, nnz_per_slot]. 
          training: boolean
                  whether training or not.
  
          Returns
          -------
          emb_vector: tf.float
                  the embedding vectors for the input keys. When dynamic_input=False, 
                  its shape is *[batchsize, slot_num, nnz_per_slot, embedding_vec_size]*.
                  Otherwise, its shape is *[None, embedding_vec_size]*, where *None* equals
                  to the size of inputs.
docstring_summary: The forward logic of this wrapper class.
docstring_tokens: The forward logic of this wrapper class .
function: def call(self, inputs, training=True):
          """
          The forward logic of this wrapper class.
  
          Parameters
          ----------
          inputs: tf.Tensor
                  keys are stored in tf.Tensor. It must be stored in row-major.
                  If `dynamic_input = True`, then inputs.shape must be [None,], 
                  otherwise, inputs.shape must be [batchsize, slot_num, nnz_per_slot]. 
          training: boolean
                  whether training or not.
  
          Returns
          -------
          emb_vector: tf.float
                  the embedding vectors for the input keys. When dynamic_input=False, 
                  its shape is *[batchsize, slot_num, nnz_per_slot, embedding_vec_size]*.
                  Otherwise, its shape is *[None, embedding_vec_size]*, where *None* equals
                  to the size of inputs.
          """
          emb_vector = embedding_ops.embedding_lookup(embedding_variable=self.var, 
                                                      values=inputs,
                                                      training=training,
                                                      dynamic_input=self.dynamic_input)
          return emb_vector
function_tokens: def call ( self , inputs , training = True ) : emb_vector = embedding_ops . embedding_lookup ( embedding_variable = self . var , values = inputs , training = training , dynamic_input = self . dynamic_input ) return emb_vector