code: def test_attention(self):
      def f(q, k, v):
          return Tensor.scaled_dot_product_attention(
              q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
          ).realize()
  
      jf = TinyJit(f)
      for i in range(1, 5):
          vi = Variable("i", 1, 10).bind(i)
          q = Tensor.rand(2, 1, 4, 8)
          k = Tensor.rand(2, i, 4, 8)
          v = Tensor.rand(2, i, 4, 8)
          symbolic = (
              jf(q, k.reshape(2, vi, 4, 8), v.reshape(2, vi, 4, 8))
              .reshape(2, 4, 1, 8)
              .numpy()
          )
          expected = f(q, k, v).numpy()
          np.testing.assert_allclose(
              symbolic, expected, atol=1e-6, rtol=1e-6
          )
      assert_jit_cache_len(jf, 5)
  
imports: import unittest
  from test.helpers import assert_jit_cache_len
  from tinygrad.jit import TinyJit
  from tinygrad.helpers import getenv
  from tinygrad.shape.symbolic import Variable
  from tinygrad.tensor import Tensor
  import numpy
label_desc: Use image node_id 5 for calling the TestSymbolicJit obj's underlying member method code with example usage: obj.test_attention() and returns: Tensor