class_docstr: A speaker model with ResNet layers. 
class_docstr_tok: ["A","speaker","model","with","ResNet","layers","."]
code: def se_moudle(self, x, channels, reduction, name=""):
      input_t = x
      x = tf.reduce_mean(x, [1, 2], name=name + "_avg", keep_dims=True)
      x = tf.layers.conv2d(
          x,
          channels // reduction,
          (1, 1),
          use_bias=False,
          name=name + "_1x1_down",
          strides=(1, 1),
          padding="valid",
          data_format="channels_last",
          activation=None,
          kernel_initializer=tf.initializers.glorot_uniform(),
          bias_initializer=tf.zeros_initializer(),
      )
      x = tf.nn.relu(x, name=name + "_1x1_down_relu")
  
      x = tf.layers.conv2d(
          x,
          channels,
          (1, 1),
          use_bias=False,
          name=name + "_1x1_up",
          strides=(1, 1),
          padding="valid",
          data_format="channels_last",
          activation=None,
          kernel_initializer=tf.initializers.glorot_uniform(),
          bias_initializer=tf.zeros_initializer(),
      )
      x = tf.nn.sigmoid(x, name=name + "_1x1_up_sigmoid")
      return tf.multiply(input_t, x, name=name + "_mul")
  
imports: import math
  from absl import logging
  import delta.compat
  import tensorflow.keras.layers
  from delta import utils
  from delta.layers import common_layers
  from delta.models.base_model import RawModel
  from delta.utils.register import registers
  from delta.utils.loss.loss_utils import arcface_loss
  from delta.utils.hparam import HParams
label_desc: Use image node_id 5 for calling the SpeakerResNetRawModel obj's underlying member method code with example usage: obj.se_moudle(x, channels, reduction, name) and returns: tf