class_docstr: FullyConnect model for text classification based on
  pretrain embedding/model
class_docstr_tok: ["FullyConnect","model","for","text","classification","based","on","pretrain","embedding\/model"]
code: def call(self, inputs, training=None, mask=None):
      input_x = inputs["input_x"]
      if self.use_dense_task:
          dense_input = inputs["input_dense"]
  
      # [batch_size, max_len, embed_len]
      out = self.embed(input_x)
      if self.use_pretrained_model:
          logging.info(
              "use_pretrained_model: {}, {}".format(
                  self.pretrained_model_name, self.pretrained_model_mode
              )
          )
          if self.pretrained_model_name == "elmo":
              input_px = self.get_pre_train_graph(input_x)
              input_px = tf.reshape(
                  input_px,
                  [-1, self.max_len, self.pretrained_model_dim],
              )
              out = tf.concat([out, input_px], axis=-1)
              out = tf.reduce_max(out, axis=1)
          if self.pretrained_model_name == "bert":
              out = self.get_pre_train_graph(input_x)
      else:
          out = tf.reduce_max(out, axis=1)
      out = self.embed_d(out, training=training)
      if self.use_dense_input:
          dense_out = self.dense_input_linear(dense_input)
          if self.only_dense_input:
              out = dense_out
          else:
              out = tf.keras.layers.Concatenate()([out, dense_out])
      # [batch_size, class_num]
      scores = self.final_dense(out)
      return scores
  
imports: import pickle
  import delta.compat
  from absl import logging
  from delta import layers
  from delta.models.text_cls_model import TextClassModel
  from delta.utils.register import registers
  from delta import utils
label_desc: Use image node_id 2 for calling the FullyConnectModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: scores