code: def deconv2d(
      input_,
      output_shape,
      k_h,
      k_w,
      d_h,
      d_w,
      stddev=0.02,
      name="deconv2d",
  ):
      """Deconvolution layer."""
      with tf.variable_scope(name):
          w = tf.get_variable(
              "w",
              [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
              initializer=tf.random_normal_initializer(stddev=stddev),
          )
          deconv = tf.nn.conv2d_transpose(
              input_,
              w,
              output_shape=output_shape,
              strides=[1, d_h, d_w, 1],
          )
          biases = tf.get_variable(
              "biases",
              [output_shape[-1]],
              initializer=tf.constant_initializer(0.0),
          )
          return tf.reshape(
              tf.nn.bias_add(deconv, biases), deconv.get_shape()
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling a global function with example usage: deconv2d(input_, output_shape, k_h, k_w, d_h, d_w, stddev, name) and returns: tf