code: def _apply_encoder_layer(
      translation_layer, output_depth, nonpadding_list
  ):
      """Applies an encoder layer with basic arguments."""
  
      input_tensor = (
          tf.random_uniform(
              [_BATCH_SIZE, _TOTAL_SEQUENCE_LENGTH, _INPUT_DEPTH]
          )
          / 4.0
      )
      nonpadding = tf.constant(nonpadding_list)
      residual_tensor = tf.random_uniform(
          [_BATCH_SIZE, _TOTAL_SEQUENCE_LENGTH, output_depth]
      )
      hparams = transformer.transformer_base()
  
      return translation_layer.apply_layer(
          input_tensor,
          residual_tensor,
          output_depth,
          tf.nn.relu,
          hparams,
          "",
          mask_future=False,
          nonpadding=nonpadding,
          layer_preprocess_fn=None,
          postprocess_dropout=True,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import itertools
  from absl.testing import parameterized
  import numpy
  from tensor2tensor.layers import common_attention
  from tensor2tensor.models import transformer
  from tensor2tensor.models.neural_architecture_search import nas_layers
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_encoder_layer(translation_layer, output_depth, nonpadding_list) and returns: translation_layer