%0 = stablehlo.convert %arg0 : (tensor<1x4x2xf32>) -> tensor<1x4x2xi8>
    return %0 : tensor<1x4x2xi8>
  }
// CHECK: @uniform_quantize_0
  func.func private @uniform_quantize_1(%arg0: tensor<1x4x3xf32>, %arg1: tensor<1x1x1xf32>, %arg2: tensor<1x1x1xi8>) -> tensor<1x4x3xi8> {
    %0 = stablehlo.convert %arg0 : (tensor<1x4x3xf32>) -> tensor<1x4x3xi8>
    return %0 : tensor<1x4x3xi8>
  }

    // CHECK-NEXT: %[[CMP:.*]] = mhlo.compare GT, %[[INP:.*]], %[[ZERO]], NOTYPE : (tensor<1x4x4xf32>, tensor<1x4x4xf32>) -> tensor<1x4x4xi1>
    // CHECK-NEXT: %[[RES:.*]] = mhlo.select %[[CMP]], %[[GRADIENT]], %[[LEAKYGRAD]] : tensor<1x4x4xi1>, tensor<1x4x4xf32>
    // CHECK-NEXT: return %[[RES]] : tensor<1x4x4xf32>

      %0 = "tf.XlaCallModule"(%arg0, %cst_0, %cst_1) <{Sout = [#tf_type.shape<1x4x4x3>], module = "", version = 9 : i64}> {_entry_function = @composite_fn_1, _original_entry_function = "composite_fn_1", _tfl_quant_trait = "fully_quantizable"} : (tensor<1x4x4x3xf32>, tensor<3x1x1x3xf32>, tensor<3xf32>) -> tensor<1x4x4x3xf32>

func.func @CheckNumericVerifyWholeModel(%arg0: tensor<1x4x4x3xf32>) -> tensor<1x1x1x3xf32> {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>, volatile} : (tensor<1x4x4x3xf32>) -> tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>
  %1 = "tfl.dequantize"(%0) : (tensor<1x4x4x3x!quant.uniform<i8:f32, 0.1>>) -> tensor<1x4x4x3xf32>

  // CHECK: return %[[ADD]] : tensor<14xf32>
}

func.func @scatter_nd_updates_bool(%arg0: tensor<1x24xi1>, %arg1: tensor<1x2x2xi32>, %arg2: tensor<1x2xi1>) -> tensor<1x24xi1> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<1x24xi1>, tensor<1x2x2xi32>, tensor<1x2xi1>) -> tensor<1x24xi1>
  func.return %0 : tensor<1x24xi1>

// CHECK-LABEL: scatter_nd_updates_bool(

  %1 = "tfr.cast"(%arg0) : (!tfr.tensor) -> tensor<1x4x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>
  %2 = "tfr.cast"(%arg1) : (!tfr.tensor) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>
  %3 = "tfr.cast"(%2) : (tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>) -> !tfr.tensor
  %4 = "tfr.cast"(%1) : (tensor<1x4x4x3x!quant.uniform<i8:f32, 0.0078420601785182952:-1>>) -> !tfr.tensor

  %2 = stablehlo.uniform_quantize %1 : (tensor<1x4x4x4x!quant.uniform<i32:f32:3, {6.000000e+00, 6.000000e+00, 6.000000e+00, 6.000000e+00}>>) -> tensor<1x4x4x4x!quant.uniform<i8:f32, 8.000000e+00:-128>>
  return %2 : tensor<1x4x4x4x!quant.uniform<i8:f32, 8.000000e+00:-128>>
}

// CHECK:           %[[VAL_0:.*]] = "tf.All"(%[[ARG_0]], %[[DIMENSIONS]]) <{keep_dims = false}> : (tensor<1x2x3x4x5xi1>, tensor<2xi64>) -> tensor<2x4x5xi1>
// CHECK:           return %[[VAL_0:.*]] : tensor<2x4x5xi1>
// CHECK:         }
func.func @convert_reduce_to_all(%arg0: tensor<1x2x3x4x5xi1>, %arg1: tensor<2xi64>) -> tensor<2x4x5xi1> {
  %0 = mhlo.constant dense<true> : tensor<i1>
  %1 = "mhlo.reduce"(%arg0, %0) ({

  %axis_0 = arith.constant dense<0> : tensor<1xi32>
  %axis_1 = arith.constant dense<1> : tensor<1xi32>
  %axis_2 = arith.constant dense<2> : tensor<1xi32>

  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<1xi32>, tensor<1x2xi32>) -> tensor<1x2xi32>
  func.return %0: tensor<1x2xi32>
}

// CHECK-LABEL: func @broadcast_multi_dim_add
// TODO(laurenzo): Change this to a (4x1x1 + 1x4x4x4) shaped add once upstream
// broadcastable bug is fixed (helps make the CHECK matching unambiguous)
func.func @broadcast_multi_dim_add(%arg0: tensor<4x1x1xi32>, %arg1: tensor<4x4x4x4xi32>) -> tensor<4x4x4x4xi32> {