code: def get_data(self):
      x = tf.random_normal(
          (BATCH_SIZE, TARGET_LENGTH, N_CHANNELS), dtype=DTYPE
      )
      x_lengths = np.random.randint(
          low=1, high=TARGET_LENGTH + 1, size=BATCH_SIZE
      )
      x_lengths = np.ceil(x_lengths / 4.0) * 4.0
      x_lengths = x_lengths.astype(int)
      x_mask = tf.sequence_mask(
          x_lengths, maxlen=TARGET_LENGTH, dtype=DTYPE
      )
      return x, x_mask, x_lengths
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import tempfile
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import transformer_glow_layers
  from tensor2tensor.layers import transformer_glow_layers_ops
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the TransformerGlowLayersTest obj's underlying member method code with example usage: obj.get_data() and returns: x,  x_mask,  x_lengths