%cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<10xf32>} : () -> tensor<10xf32>
    %cst_0 = "tf.Const"() {value = dense<[-1, 10]> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.MatMul"(%arg0, %arg1) {
      transpose_a = false, transpose_b = false
    } : (tensor<1x10xf32>, tensor<10x10xf32>) -> tensor<1x10xf32>  loc(fused["MatMul:", "test_opt_out"])

  constexpr absl::string_view kProgram = R"mlir(
func.func @main(
    %arg0: tensor<10x10xf32>, %scale: tensor<10xf32>, %zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.UniformQuantize"(%arg0, %scale, %zp) {
    quantization_axis = 1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x10xf32>, tensor<10xf32>, tensor<10xi32>) -> tensor<10x10x!tf_type.qint8>

    // CHECK-NEXT: return %[[DIV]] : tensor<4x10xf32>
    %0 = "tf.Softsign"(%arg0) : (tensor<4x10xf32>) -> tensor<4x10xf32>
    func.return %0 : tensor<4x10xf32>
}

// -----

// CHECK-LABEL: func @softsign_grad
func.func @softsign_grad(%arg0: tensor<4x10xf32>, %arg1: tensor<4x10xf32>) -> tensor<4x10xf32> {

    // CHECK-NEXT: %[[ONE:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>

    : ( tensor<1x28x28xf32>,
        tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>, tensor<20x28xf32>,
        tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>, tensor<20x20xf32>,
        none, none, none,
        tensor<20xf32>, tensor<20xf32>, tensor<20xf32>, tensor<20xf32>,
        none, none,
        tensor<1x20xf32>, tensor<1x20xf32>,

      %arg6 : tensor<!tf_type.resource<tensor<640xf32>>>,
      %arg7 : tensor<!tf_type.resource<tensor<512x640xf32>>>,
      %arg8 : tensor<!tf_type.resource<tensor<640x640xf32>>>,
      %arg9 : tensor<!tf_type.resource<tensor<4096xf32>>>,
      %arg10 : tensor<!tf_type.resource<tensor<640x4096xf32>>>,
      %arg11 : tensor<!tf_type.resource<tensor<512xf32>>>
      ) {

  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>

    }
  }
  return new_arg_type;
}

// Combination of value producer and port of value produced (e.g.,
//   <value result output>:<value in output tensor>,
// so for tf.Const -> tensor<10x20xf32>, [0,2,18] would point to a unique output
// scalar value).
struct ValuePort {
  PointerUnion<Operation*, BlockArgument> producer;
  SmallVector<unsigned int, 2> port;

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])