code: def test_startsym():
      # create a sample input tensor with shape [B, seq_len, hidden_dim] hidden_dim=C
      B, seq_len, hidden_dim = 2, 3, 4
      x = torch.randn(B, seq_len, hidden_dim)
  
      # create an instance of the StartSym class with num_channels=3
      start_sym = StartSym(hidden_dim=hidden_dim)
  
      # verify that the sym parameter has the correct shape and is initialized
      # with random uniform values in orrect domain
      assert start_sym.sym.shape == torch.Size([hidden_dim])
      assert start_sym.sym.min() > -3 and start_sym.sym.max() < 3.0
  
      # prefix x with the learned start symbol
      y = start_sym(x)
  
      # verify that y has the correct shape
      assert y.shape == x.shape
      # verify that y is prefixed with the learned start symbol
      assert all(
          [
              torch.equal(y[i, 0, :], start_sym.sym)
              for i in range(y.shape[0])
          ]
      )
      assert torch.equal(y[:, 1:, :], x[:, :-1, :])
      # test the initial symbol is always the same
      assert all(
          [
              (start_sym.sym == StartSym(hidden_dim).sym).all()
              for _ in range(3)
          ]
      )
  
imports: import pytest
  import torch
  from projects.torch_vct.neural.entropy_model_layers import LearnedPosition, StartSym, TransformerBlock
label_desc: Use image node_id 2 for calling a global function with example usage: test_startsym() without return types