def METHOD_NAME(self):
    torch.manual_seed(0)
    model_kwargs = {
        "num_attention_heads": 2,
        "attention_head_dim": 12,
        "embedding_dim": self.text_embedder_hidden_size,
        "num_layers": 1,
    }
    model = PriorTransformer(**model_kwargs)
    # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
    model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))
    return model