class_docstr: Transformer that predicts blocks of the output in parallel.
code: def top(self, body_output, features):
      assert self._hparams.block_size > 0
  
      if (
          self._hparams.mode == tf_estimator.ModeKeys.TRAIN
          or self._hparams.mode == tf_estimator.ModeKeys.EVAL
      ):
          if self._hparams.mode == tf_estimator.ModeKeys.TRAIN:
              features["block_index"] = tf.random_uniform(
                  shape=[],
                  minval=0,
                  maxval=self._hparams.block_size,
                  dtype=tf.int64,
              )
          else:
              features["block_index"] = 0
          k = features["block_index"]
          body_output = body_output[:, :, k : k + 1, :]
  
      return super(TransformerBlockParallel, self).top(
          body_output, features
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling the TransformerBlockParallel obj's underlying member method code with example usage: obj.top(body_output, features) and returns: super