# CHECK: "tf_saved_model.global_tensor"() <{sym_name = "[[CONST:[a-zA-Z_0-9.]+]]", type = tensor<f32>, value = dense<4.300000e+01> : tensor<f32>}> {tf_saved_model.exported_names = [{{.*}}]} : () -> ()

  # CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR:[a-zA-Z_0-9]+]]", type = tensor<f32>, value = dense<4.200000e+01> : tensor<f32>}> {tf_saved_model.exported_names = ["v42"]} : () -> ()
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(

func.func @reduce_window_f32(%arg0: tensor<2x3x1x3xf32>) -> tensor<2x3x1x3xf32> {
  // CHECK-NOT: f32
  // CHECK: stablehlo.reduce_window
  %0 = stablehlo.constant dense<0.0> : tensor<f32>
  %1 = "stablehlo.reduce_window"(%arg0, %0) ({
    ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>):
      %2 = stablehlo.maximum %arg1, %arg2 : tensor<f32>
      stablehlo.return %2 : tensor<f32>

  %0 = stablehlo.convolution(%arg0, %arg1) dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {pad = [[1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x3x3x!quant.uniform<i8:f32, 1.000000e+0:-100>>, tensor<2x4x3x3x!quant.uniform<i8:f32:0,...

// CHECK-NOT: func @_func
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "arith.constant"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
      %outputs_0, %control_1 = tf_executor.island wraps "arith.constant"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>

  // CHECK-NEXT: [[r2:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.Tanh"([[r1]]) {T = f32}
  %4 = "tf.Tanh"(%3) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK-NOT: tf.Identity
  %5 = "tf.Identity"(%4) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0"} : (tensor<3x3xf32>) -> tensor<3x3xf32>

  // CHECK: device = "xpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>

}

// CHECK-LABEL: @splat_scalar_no_change
func.func @splat_scalar_no_change() -> (tensor<f32>, tensor<i32>) {
  // CHECK-NOT: mhlo.broadcast_in_dim
  %cst0 = mhlo.constant dense<0.000000e+00> : tensor<f32>
  %cst1 = mhlo.constant dense<0> : tensor<i32>
  func.return %cst0, %cst1 : tensor<f32>, tensor<i32>

// CHECK-NEXT:     signature_defs: [ ]
// CHECK-NEXT:   }

// IMPORT: "tfl.fake_quant"(%arg0) <{max = 1.400000e+00 : f32, min = 3.000000e-01 : f32, narrow_range = false, num_bits = 6 : i32}>

  %0 = "tfl.fake_quant"(%arg0) {num_bits = 6 : i32, narrow_range = false, min = 0.3:f32, max = 1.4:f32} : (tensor<4 x f32>) -> tensor<4 x f32>
  func.return %0 : tensor<4xf32>

  %0 = corert.const_dense_tensor dense<0.0> : tensor<f32>
  // CHECK: tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor
  %1 = tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor %0 {_tfrt_cost = 1 : i64, device = "cpu"} : (!corert.tensorhandle) -> (!tfrt_fallback.tf_tensor)
  // CHECK: tfrt_fallback_async.executeop key(0) cost(100) device("cpu") "tf.Relu"(%{{.*}}) {T = f32} : 1

// 8-bit integer and expressed type is f32.
bool IsI8F32UniformQuantizedType(Type type);

// Returns true iff `type` is a uniform quantized per-axis (per-channel) type
// whose storage type is 8-bit integer and expressed type is f32.
bool IsI8F32UniformQuantizedPerAxisType(Type type);

// Returns true iff `type` is a uniform quantized type whose storage type is
// 32-bit integer and expressed type is f32.