func.return %0 : tensor<10x10xf32>
  // CHECK-LABEL: strided_slice_with_constant_attributes
  // CHECK-DAG: [[BEGIN:%cst.*]] = arith.constant dense<[-1, 0, 0]> : tensor<3xi32>
  // CHECK-DAG: [[END:%cst.*]] = arith.constant dense<[0, 10, 10]> : tensor<3xi32>

//   %cst = arith.constant dense<1.0> : tensor<16x16x4xf32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<16x16x4xf32>)
// After this optimization:
//   %cst = arith.constant dense<1.0> : tensor<f32>
//   %0 = "tfl.conv_2d"...
//   %1 = "tfl.add"(%0, %cst) : (tensor<16x16x4xf32>, tensor<f32>)

mlir_module = '''python
func @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {
   %add = "magic.op"(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>
   return %ret : tensor<10x10xf32>
}
'''

@tf.function
def foo(x, y):
  return mlir_passthrough_op([x, y], mlir_module, Toutputs=[tf.float32])