code: def deconv3d(
      input_tensor,
      output_shape,
      kernel_size=(5, 5, 5),
      stride_size=(2, 2, 2),
      stddev=0.02,
      name="deconv3d",
      with_w=False,
  ):
      with tf.variable_scope(name):
          # filter : [height, width, output_channels, in_channels]
  
          w = tf.get_variable(
              "w",
              [
                  kernel_size[0],
                  kernel_size[1],
                  kernel_size[2],
                  output_shape[-1],
                  input_tensor.get_shape()[-1],
              ],
              initializer=tf.contrib.layers.xavier_initializer(),
          )
  
          try:
              deconv = tf.nn.conv3d_transpose(
                  input_tensor,
                  w,
                  output_shape=output_shape,
                  strides=[
                      1,
                      stride_size[0],
                      stride_size[1],
                      stride_size[2],
                      1,
                  ],
              )
  
          # Support for verisons of TensorFlow before 0.7.0
          except AttributeError:
              deconv = tf.nn.deconv3d(
                  input_tensor,
                  w,
                  output_shape=output_shape,
                  strides=[
                      1,
                      stride_size[0],
                      stride_size[1],
                      stride_size[2],
                      1,
                  ],
              )
  
          biases = tf.get_variable(
              "biases",
              [output_shape[-1]],
              initializer=tf.zeros_initializer(),
          )
          deconv = tf.reshape(
              tf.nn.bias_add(deconv, biases), deconv.get_shape()
          )
  
          if with_w:
              return deconv, w, biases
          else:
              return deconv
  
imports: import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: deconv3d(input_tensor, output_shape, kernel_size, stride_size, stddev, name, with_w) and returns: deconv,  w,  biases, deconv