// CHECK:           %[[VAL_8:.*]] = "tf.Select"(%[[VAL_2]], %[[VAL_3]], %[[VAL_7]]) : (tensor<1x2x3x4xi1>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32>
// CHECK:           return %[[VAL_8]] : tensor<1x2x3x4xf32>
// CHECK:         }
func.func @sign(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32> {

  func.return %1 : tensor<1x2x2x5xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.strided_slice"(%0, %arg1, %arg2, %arg3)
// CHECK: %2 = "tfl.quantize"(%1) <{qtype = tensor<1x2x2x5x!quant.uniform<u8:f32, 1.000000e-01>>}> {volatile}
// CHECK: %3 = "tfl.dequantize"(%2)

  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>
  %2 = "tfl.quantize"(%1) {qtype = tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>

func.func @attribute_cast(%arg0: tensor<1x4x4x1xf32>) -> tensor<1x2x2x1xf32> {
  %0 = "tfr.cast"(%arg0) : (tensor<1x4x4x1xf32>) -> !tfr.tensor
  %stride_i32 = arith.constant 2 : i32
  %1 = tfr.call @tf__my_max_pool(%0, %stride_i32, %stride_i32) : (!tfr.tensor, i32, i32) -> !tfr.tensor
  %2 = "tfr.cast"(%1) : (!tfr.tensor) -> tensor<1x2x2x1xf32>
  func.return %2 : tensor<1x2x2x1xf32>
// CHECK: tf__max_pool
}

  %0 = "tf.Pack"(%arg0, %arg1) : (tensor<3x5xf32>, tensor<3x5xf32>) -> tensor<2x3x5xf32>
  func.return %0 : tensor<2x3x5xf32>
}

// CHECK-LABEL: func @square
func.func @square(%arg0: tensor<3xf32>) -> tensor<3xf32> {
  // CHECK: "tf.Mul"(%arg0, %arg0)
  %1 = "tf.Square"(%arg0) : (tensor<3xf32>) -> tensor<3xf32>
  func.return %1 : tensor<3xf32>
}

    rhs_quantization_max_val = 127 : i64
  } : (tensor<1x2x2x3xf32>, tensor<2x3x3x2x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1x2x1x2xf32>
  func.return %0 : tensor<1x2x1x2xf32>
}

// -----

// CHECK-LABEL: func @uniform_quantized_convolution_hybrid_explicit
func.func @uniform_quantized_convolution_hybrid_explicit(%input: tensor<1x2x2x3xf32>) -> tensor<1x3x3x2xf32> {

// -----

// CHECK-LABEL: func @sign
// CHECK-SAME: [[ARG:%arg.*]]: tensor<1x2x3x4xf32>
func.func @sign(%arg0: tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32> {
  // CHECK: [[SIGN:%.*]] = mhlo.sign [[ARG]]
  // CHECK: return [[SIGN]] : tensor<1x2x3x4xf32>
  %0 = "tf.Sign"(%arg0) : (tensor<1x2x3x4xf32>) -> (tensor<1x2x3x4xf32>)
  func.return %0 : tensor<1x2x3x4xf32>
}

// -----

// CHECK-LABEL: func @sign_dynamic

    quantization_axis = -1 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<1x2x2x1x!tf_type.qint32>, tensor<f32>, tensor<i32>) -> tensor<1x2x2x1xf32>

  return %output : tensor<1x2x2x1xi32>

  //
  // For example,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %height = tf.StridedSlice(%shape, 1, 2, 1)
  //
  // In this case %height can be replaced with a constant 2.
  //
  // Or,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %spatial_shape = tf.StridedSlice(%shape, 1, 3, 1)
  //