class_docstr: CNN model for text classification.
class_docstr_tok: ["CNN","model","for","text","classification","."]
code: def call(self, inputs, training=None, mask=None):
      input_x = tf.identity(inputs["input_x"], name="input_x")
      if self.use_dense_task:
          dense_input = inputs["input_dense"]
      embed = self.embed(input_x)
      embed_expand = tf.expand_dims(embed, axis=-1)
      conv_outs = [conv2d(embed_expand) for conv2d in self.conv2ds]
      pool_outs = [pool(co) for co, pool in zip(conv_outs, self.pools)]
      out = tf.keras.layers.Concatenate(axis=1)(pool_outs)
      out = self.flat(out)
      out = self.dropout(out, training=training)
      out = self.dense(out)
      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])
      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 SeqclassCNNModel obj's underlying member method code with example usage: obj.call(inputs, training, mask) and returns: scores