code: def encode(x, x_space, hparams, name):
      """Transformer preparations and encoder."""
      with tf.variable_scope(name):
          (
              encoder_input,
              encoder_self_attention_bias,
              ed,
          ) = transformer.transformer_prepare_encoder(
              x, x_space, hparams
          )
          encoder_input = tf.nn.dropout(
              encoder_input, 1.0 - hparams.dropout
          )
          return (
              transformer.transformer_encoder(
                  encoder_input, encoder_self_attention_bias, hparams
              ),
              ed,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import math
  import os
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import latent_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import beam_search
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import expert_utils
  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 7 for calling a global function with example usage: encode(x, x_space, hparams, name) and returns: transformer,  ed