code: def forward(self, inputs):
      x, y = inputs
      x = tf.reshape(x, [-1, self.in_joints, self.in_F])  # [N, J, 3]
      y = tf.reshape(y, [-1, self.out_joints, 3])  # [N, J, 3]
      y = tf.concat(
          [
              x[:, :, :2] + y[:, :, :2],
              tf.expand_dims(y[:, :, 2], axis=-1),
          ],
          axis=2,
      )  # [N, J, 3]
      y = tf.reshape(y, [-1, self.out_joints * 3])
      return y
  
imports: import numpy
  import tensorflow
  from tensorlayer.layers import Layer, Dropout, Dense, Input, BatchNorm, Reshape, Elementwise
  from tensorlayer.models import Model
  from tensorlayer import logging
  from .common import mask_weight, neighbour_matrix
label_desc: Use image node_id 3 for calling the End_layer obj's underlying member method code with example usage: obj.forward(inputs) and returns: y