def dummy_input(self):
        """
        Args:
            None
        Returns:
            Dict: Dictionary of dummy input tensors
        """
        batch_size = 2  # N
        num_channels = 4  # C
        height = width = 16  # H, W
        embedding_dim = 32  # D
        sequence_length = 16  # L

        hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
        encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
        timestep = torch.rand(size=(batch_size,)).to(torch_device)
        encoder_mask = torch.randn(size=(batch_size, sequence_length)).to(torch_device)
        image_rotary_emb = torch.randn((384, 384, 4)).to(torch_device)

        return {
            "hidden_states": hidden_states,
            "encoder_hidden_states": encoder_hidden_states,
            "timestep": timestep,
            "encoder_mask": encoder_mask,
            "image_rotary_emb": image_rotary_emb,
            "cross_attention_kwargs": {},
        }

        Args:
            None
        Returns:
            Dict: Dictionary of dummy input tensors
        
tests/models/transformers/test_models_transformer_lumina.py