code: def test_transformerblock(num_heads, hidden_dim, mlp_dim):
      batch_size = 2
      seq_length = 16
      # create an instance of the TransformerBlock
      transformer_block = TransformerBlock(
          seq_length=seq_length,
          num_heads=num_heads,
          hidden_dim=hidden_dim,
          mlp_dim=mlp_dim,
          drop_out_rate=0.1,
          drop_path_rate=0.1,
          is_decoder=False,
      )
      input = torch.randn(batch_size, seq_length, hidden_dim)
      output = transformer_block(input)
      # assert that the output has the expected shape
      assert output.shape == torch.Size(
          [batch_size, seq_length, hidden_dim]
      )
  
imports: import pytest
  import torch
  from projects.torch_vct.neural.entropy_model_layers import LearnedPosition, StartSym, TransformerBlock
label_desc: Use image node_id 4 for calling a global function with example usage: test_transformerblock(num_heads, hidden_dim, mlp_dim) without return types