code: def _apply_decoder_layer(
      translation_layer, input_tensor, output_depth, encoder_depth
  ):
      """Applies an decoder layer with basic arguments."""
  
      residual_tensor_values = (
          np.random.rand(
              *[_BATCH_SIZE, _TOTAL_SEQUENCE_LENGTH, output_depth]
          )
          - 0.5
      )
      residual_tensor = tf.constant(
          residual_tensor_values, dtype=tf.float32
      )
      encoder_output_values = (
          np.random.rand(
              *[_BATCH_SIZE, _TOTAL_SEQUENCE_LENGTH, encoder_depth]
          )
          - 0.5
      )
      encoder_output = tf.constant(
          encoder_output_values, dtype=tf.float32
      )
      encoder_cell_outputs = [encoder_output] * _NUM_CELLS
      hparams = transformer.transformer_base()
      hparams.attention_dropout = 0
      decoder_self_attention_bias = (
          common_attention.attention_bias_lower_triangle(
              _TOTAL_SEQUENCE_LENGTH
          )
      )
  
      output_tensor = translation_layer.apply_layer(
          input_tensor,
          residual_tensor,
          output_depth,
          None,
          hparams,
          "",
          nonpadding=None,
          mask_future=True,
          layer_preprocess_fn=None,
          postprocess_dropout=False,
          decoder_self_attention_bias=decoder_self_attention_bias,
          encoder_decoder_attention_bias=None,
          encoder_cell_outputs=encoder_cell_outputs,
          cell_number=_CELL_NUMBER,
      )
  
      return output_tensor
  
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 2 for calling a global function with example usage: _apply_decoder_layer(translation_layer, input_tensor, output_depth, encoder_depth) and returns: output_tensor