class_docstr: Attention net.  See file docstring.
docstr: Process the transformer encoder inputs.
docstr_tok: ["Process","the","transformer","encoder","inputs","."]
code: def _prepare_encoder(self, inputs, target_space):
      """Process the transformer encoder inputs."""
      inputs = common_layers.flatten4d3d(inputs)
  
      output = transformer.transformer_prepare_encoder(
          inputs,
          target_space,
          self._hparams,
          features=None,
      )
      enco_input, enco_self_att_bias, enco_deco_att_bias = output
  
      enco_input = tf.nn.dropout(
          enco_input, 1.0 - self._hparams.layer_prepostprocess_dropout
      )
  
      return enco_input, enco_self_att_bias, enco_deco_att_bias
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  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 3 for calling the TransformerMoe obj's underlying member method code with example usage: obj._prepare_encoder(inputs, target_space) and returns: enco_input,  enco_self_att_bias,  enco_deco_att_bias