# CHECK:      func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME:   [[ARG0:%.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]},
# CHECK-SAME:   [[ARG1:%.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @[[VAR]]})
# CHECK-SAME:             -> (tensor<3x3xf32> {tf_saved_model.index_path = ["r"]})
# CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["key"]

// CHECK-LABEL: func @dot_int4
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<?x3xf32>) -> tensor<?x2xf32>
func.func @dot_int4(%arg0: tensor<?x3xf32>) -> tensor<?x2xf32> {
  // CHECK: %[[cst:.*]] = stablehlo.constant
  // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[cst]])
  // CHECK-SAME: quant.uniform<i8:f32, 0.0040316890267764818:127>
  // CHECK: %[[dq1:.*]] = "quantfork.dcast"(%[[q1]])
  // CHECK-SAME: quant.uniform<i8:f32, 0.0040316890267764818:127>

// CHECK-LABEL: func @testSingleConj
// CHECK-SAME:  ([[ARG0:%.+]]: tensor<complex<f32>>)
func.func @testSingleConj(%arg0: tensor<complex<f32>>) -> tensor<complex<f32>> {
  // CHECK: [[CONJ:%.+]] = "tf.Conj"([[ARG0]])
  %0 = "tf.Conj"(%arg0) : (tensor<complex<f32>>) -> tensor<complex<f32>>
  // CHECK: return [[CONJ]]
  func.return %0: tensor<complex<f32>>
}

// CHECK-LABEL: func @testDoubleConj
// CHECK-SAME:  ([[ARG0:%.+]]: tensor<complex<f32>>)

  // FB-SAME:  -8.0, -9.0, -10.0, -11.0, -12.0, -13.0, -14.0, -15.0, -16.0,
  // FB-SAME:  -17.0, -18.0, -19.0, -20.0, -21.0, -22.0, -23.0, -24.0, -25.0,
  // FB-SAME:  -26.0, -27.0, -28.0, -29.0, -30.0, -31.0, -32.0, -33.0, -34.0,
  // FB-SAME:  -35.0, -36.0, -37.0, -38.0, -39.0 ],
  // FB-NEXT:        max: [ 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,

func.func @transposeFusedBatchNormV3_f32(
  %arg0: tensor<1x28x28x64xf32>,
  %arg1: tensor<64xf32>
) -> tensor<1x28x28x64xf32> {

  // CHECK: "tf.FusedBatchNormV3"
  // CHECK-SAME: (%[[X_TRANSPOSE:[0-9]*]], %arg1, %arg1, %arg1, %arg1)
  // CHECK-SAME: data_format = "NCHW"
  %y, %batch_mean, %batch_var, %reserve_1, %reserve_2, %reserve_3
    = "tf.FusedBatchNormV3"(%arg0, %arg1, %arg1, %arg1, %arg1)
       {

// CHECK: "tf.PartitionedCall"
// CHECK-SAME: f = @composite_depthwise_conv2d_with_bias_and_relu6_fn_1
// Check that the `_tfl_quant_trait` attribute has been removed.
// CHECK-NOT: _tfl_quant_trait = "fully_quantizable"

// CHECK-LABEL: private @composite_depthwise_conv2d_with_bias_and_relu6_fn_1
// CHECK: %[[DEPTHWISECONV2D_0:.*]] = "tf.DepthwiseConv2dNative"(%arg0, %arg1)

  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<f32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},
  # CHECK-SAME:   %arg1: tensor<!tf_type.resource<tensor<f32>>>
  # CHECK-SAME:   %arg2: tensor<!tf_type.resource<tensor<f32>>>
  # CHECK-SAME:   tensor<f32> {tf_saved_model.index_path = []})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["some_function"]

  func.return %3 : tensor<4x8xf32>
}

// CHECK-LABEL:   func @select(
// CHECK-SAME:                 %[[VAL_0:.*]]: tensor<2xi1>,
// CHECK-SAME:                 %[[VAL_1:.*]]: tensor<2xi32>,
// CHECK-SAME:                 %[[VAL_2:.*]]: tensor<2xi32>) -> tensor<2xi32> {

# Test importing a saved model with 2 signatures that are using a same
# BatchFunction Op, which references to a same inference_func from graph_def
# library. The result should be that both signatures uses the same
# BatchFunction Op (the shared_name is the same) and the same copy of
# inference_func.

# CHECK: f = @inference_func[[post_fix:[^,]*]]
# CHECK-SAME: shared_name = "batch"
# CH#CK: sym_name = "predict0"

// TODO(b/316226111): the printer may not guarantee the same order of fields. Rewrite the checks to be less sensitive to proto serialization formats.
// -----
// Non-SPMD: one input and one output
//
// CHECK-LABEL: func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x3xf32> {
// CHECK-NEXT:  "tf.IfrtCall"(%arg0)
// CHECK-SAME:       {program_id = [[PROGRAM_ID:.*]] : i64, variable_arg_indices = []}
// CHECK-SAME:       (tensor<1x3xf32>) -> tensor<1x3xf32>