class_docstr: main model 
class_docstr_tok: ["main","model"]
docstr: fc layers
docstr_tok: ["fc","layers"]
code: def dense_layer(self, x):
      """fc layers"""
      with tf.variable_scope("dense"):
          shape = x.shape[-1].value
          y = common_layers.linear(
              x, "dense-matmul", [shape, self.netconf["hidden1"]]
          )
          if self.netconf["use_bn"]:
              y = tf.layers.batch_normalization(
                  y,
                  axis=-1,
                  momentum=0.99,
                  training=self.train,
                  name="dense-bn",
              )
          y = tf.nn.relu6(y)
          if self.netconf["use_dropout"]:
              y = tf.layers.dropout(
                  y, self.netconf["dropout_rate"], training=self.train
              )
      return y
  
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 9 for calling the EmoCRNNRawModel obj's underlying member method code with example usage: obj.dense_layer(x) and returns: y