code: def test_transformer_stack(
      input_batch_size: int,
      input_seq_size: int,
      input_hidden_size: int,
      output_size: int,
      heads: int,
      num_layers: int,
  ):
      # make repeatable
      set_random_seed(RANDOM_SEED)
  
      # Generate synthetic data
      current_inputs = torch.normal(
          0,
          1,
          size=[input_batch_size, input_seq_size, input_hidden_size],
          dtype=torch.float32,
      )
  
      # instantiate feed forward attention reducer
      transformer_stack = TransformerStack(
          input_hidden_size,
          input_seq_size,
          input_hidden_size,
          heads,
          output_size,
          num_layers,
      )
  
      result = transformer_stack(current_inputs)
  
      # ensure returned tensor is the correct shape
      assert list(result.shape) == [
          input_batch_size,
          input_seq_size,
          input_hidden_size,
      ]
  
      # check for parameter updating if fully connected layer is present
      target = torch.randn(result.shape)
      fpc, tpc, upc, not_updated = check_module_parameters_updated(
          transformer_stack,
          (current_inputs,),
          target,
      )
      assert (
          upc == tpc
      ), f"Some parameters not updated.  These parameters not updated: {not_updated}"
  
file_path: ludwig/tests/ludwig/modules/test_attention.py
imports: import pytest
  import torch
  from ludwig.modules.attention_modules import FeedForwardAttentionReducer, MultiHeadSelfAttention, TransformerBlock, TransformerStack
  from ludwig.utils.misc_utils import set_random_seed
  from tests.integration_tests.parameter_update_utils import check_module_parameters_updated
label_desc: Use image node_id 4 for calling a global function with example usage: test_transformer_stack(input_batch_size, input_seq_size, input_hidden_size, output_size, heads, num_layers) without return types