class_docstr: main model 
class_docstr_tok: ["main","model"]
docstr: text embbeding layers
docstr_tok: ["text","embbeding","layers"]
code: def text_layer(self, x, input_text):
      """text embbeding layers"""
      with tf.variable_scope("text"):
          embedding_chars_expanded = common_layers.embedding_look_up(
              input_text, self.vocab_size, self.netconf["embedding_dim"]
          )
          h_pool_flat = common_layers.conv_pool(
              embedding_chars_expanded,
              list(map(int, self.netconf["filter_sizes"])),
              self.netconf["embedding_dim"],
              self.netconf["num_filters"],
              input_text.shape[1],
          )
          outputs = tf.concat((x, h_pool_flat), axis=1)
      return outputs
  
imports: from absl import logging
  import delta.compat
  from delta import utils
  from delta.layers import common_layers
  from delta.data.feat.tf_speech_feature import speech_params, extract_feature
  from delta.models.base_model import RawModel
  from delta.utils.register import registers
label_desc: Use image node_id 8 for calling the EmoCRNNRawModel obj's underlying member method code with example usage: obj.text_layer(x, input_text) and returns: outputs