// TF-DAG: %[[mid_scale:.*]] = "tf.Const"() <{value = dense<0.00141507247> : tensor<f32>}> : () -> tensor<f32>
// TF-DAG: %[[w1_scale:.*]] = "tf.Const"() <{value = dense<0.00477652298> : tensor<f32>}> : () -> tensor<f32>
// TF-DAG: %[[b1_scale:.*]] = "tf.Const"() <{value = dense<6.75912588E-6> : tensor<f32>}> : () -> tensor<f32>

    %cst = stablehlo.constant dense<3.000000e-01> : tensor<2x3xf32>
    %0 = "quantfork.qcast"(%cst) : (tensor<2x3xf32>) -> tensor<2x3x!quant.uniform<i8:f32, 6.000000e-03>>
    %1 = "quantfork.dcast"(%0) : (tensor<2x3x!quant.uniform<i8:f32, 6.000000e-03>>) -> tensor<2x3xf32>

  // CHECK: "tf.Iterator"
  // CHECK-SAME: output_shapes{{.*}}200x28x28x1{{.*}}200x10
  // CHECK-SAME: output_types = [f32, f32]
  // CHECK-SAME: shared_name = "_iterator1"
  %0 = "tf.AnonymousIteratorV3"() {output_shapes = [
    #tf_type.shape<200x28x28x1>,
    #tf_type.shape<200x10>], output_types = [f32, f32]} : () -> tensor<!tf_type.resource>
  // CHECK: "tf.Iterator"
  // CHECK-SAME: shared_name = "_iterator2"

  // CHECK: {{.*}} = corert.executeop.seq({{.*}}) "tf.ReadVariableOp"({{.*}}) {dtype = f32} : 1
  // CHECK: {{.*}} = corert.executeop({{.*}}) "tf.MatMul"({{.*}}) {T = f32, transpose_a = false, transpose_b = false} : 1
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:GPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>

# function. Rets that happen to coincide with a feed should have its value be
# of the feed.
#
# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>) -> (tensor<f32>, tensor<f32>, tensor<*xf32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "z:1,z:2"
# CHECK-SAME:  outputs = "z:2,z:1,a:0"

  %1 = "tf.Relu"(%0) {__op_key = 1: i32, device = "/device:CPU:0"} : (tensor<f32>) -> (tensor<f32>)
  %2 = "tf.TPUCompileMlirAndExecute"(%1, %0) {metadata = "metadata", mlir_module = "mlir_module", operandSegmentSizes = array<i32: 2, 0>, producer_name = "producer_name"} : (tensor<f32>, tensor<f32>) -> tensor<i32>
  func.return %2 : tensor<i32>
}

// -----

//===--------------------------------------------------------------------===//

// CHECK-LABEL: func @testPwStreamResults
func.func @testPwStreamResults(%arg0: tensor<f32>, %arg1: tensor<f32>) {
  "tf.PwStreamResults"(%arg0, %arg1) {names = ["foo", "bar"]} : (tensor<f32>, tensor<f32>) -> ()
  return
}

// -----
// CHECK-LABEL: func @test_ifrt_call
func.func @test_ifrt_call(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) {

    %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
    %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
    %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
    %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>

  %cst_5 = "tf.Const"() {value = dense<0.00118110236> : tensor<1xf32>} : () -> tensor<1xf32>
  %cst_6 = "tf.Const"() {value = dense<1.079500e+04> : tensor<1xf32>} : () -> tensor<1xf32>
  %cst_7 = "tf.Const"() {value = dense<0.00392156886> : tensor<f32>} : () -> tensor<f32>
  %cst_8 = "tf.Const"() {value = dense<5.000000e-01> : tensor<f32>} : () -> tensor<f32>

// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = stablehlo.constant dense<6.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_2:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK-DAG: %[[CONST_3:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2x3x3x2xf32>