%4 = "tfl.dequantize"(%3) : (tensor<32x12x!quant.uniform<u8<1:255>:f32, 0.021826678373682216:151>>) -> tensor<32x12xf32>
  %5 = "tfl.fully_connected"(%2, %4, %cst) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<32x12xf32>, tensor<32xf32>) -> tensor<1x112x112x32xf32>

func.func @foldFill() -> (tensor<3x2x1xf32>, tensor<*xf32>, tensor<*xcomplex<f32>>) {
  %0 = "tf.Const"() {value = dense<[3, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tf.Const"() {value = dense<23.0> : tensor<f32>} : () -> tensor<f32>
  // CHECK-DAG: "tf.Const"() <{value = dense<2.300000e+01> : tensor<3x2x1xf32>}>
  %2 = "tf.Fill"(%0, %1) : (tensor<3xi32>, tensor<f32>) -> tensor<3x2x1xf32>

func.func private @gelu_decomp_2(%arg0: tensor<5x10xf32>) -> tensor<5x10xf32>
func.func @gelu_aten_approximate(%arg0: tensor<5x10xf32>) -> (tensor<*xf32>) {
  %0 = mhlo.composite "aten.gelu.default" %arg0 {composite_attributes = {approximate = "tanh"}, decomposition = @gelu_decomp_2} : (tensor<5x10xf32>) -> tensor<5x10xf32>

  %2 = "tf.Transpose"(%1, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>
  func.return %2 : tensor<2x1xf32>

// CHECK: %cst = arith.constant
// CHECK: %[[trans:.*]] = "tf.Transpose"
// CHECK-SAME: -> tensor<2x1xf32>
// CHECK: %[[q:.*]] = "tfl.quantize"(%[[trans]]) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 1.000000e+00>>}>

    // 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>

  // expected-error @+1 {{'tfl.prelu' op result type '10x10' not broadcast compatible with broadcasted operands's shapes '10x10x10x10'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<10x10x10x10xf32>, tensor<10x10x10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

// -----

// CHECK:           }) : (tensor<5x10xf32>, tensor<3x1xi32>, tensor<10x3xf32>) -> tensor<5x10xf32>
// CHECK:           return %[[VAL_3]] : tensor<5x10xf32>
// CHECK:         }
func.func @convert_scatter_update_with_non_trailing_update_window_dims(
  %arg0: tensor<5x10xf32>,
  %arg1: tensor<3x1xi32>,
  %arg2: tensor<10x3xf32>) -> tensor<5x10xf32>
{
  %0 = "mhlo.scatter"(%arg0, %arg1, %arg2) ({

// execute the production code path.
func.func @main(%arg0: tensor<2x1xf32>, %arg1: tensor<2x3xf32>) -> (tensor<2x4xf32>) {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<2x1x!quant.uniform<i16:f32, 1.0>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i16:f32, 1.0>>
  %1 = "tfl.dequantize"(%0) : (tensor<2x1x!quant.uniform<i16:f32, 1.0>>) -> (tensor<2x1xf32>)

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])