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"]

    key: "T"
    value {
      type: DT_INT32
    }
  }
}
versions {
  producer: 27
}

# CHECK-LABEL: func @main
# CHECK-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<4xi32>) -> tensor<4xi32>
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "input"
# CHECK-SAME:  outputs = "output"
# CHECK-NEXT:    return %[[ARG_0]] : tensor<4xi32>

# 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>

// CHECK-SAME: tf_quant.quantized_ops = ["DepthwiseConv2D", "BiasAdd", "Relu"]
// CHECK: func private @quantized_matmul_with_bias_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu_fn
// CHECK: func private @quantized_matmul_with_bias_and_relu6_fn
// CHECK: func private @quantized_matmul_fn
// CHECK-SAME: tf_quant.quantized_ops = ["MatMul"]

    func.return %1 : tensor<100x50xf32>
  }

  // CHECK-LABEL: func @SessionInitializerFunction()
  // CHECK-SAME: tf_saved_model.exported_names = ["SessionInitializerFunction"]
  // CHECK-SAME: tf_saved_model.initializer_type = "restore_op"
  // CHECK: %[[VAR:.*]] = "tf.VarHandleOp"
  // CHECK-SAME: "var1"
  // CHECK: %[[CST:.*]] = arith.constant dense<> : tensor<0xf32>
  // CHECK: "tf.AssignVariableOp"(%[[VAR]], %[[CST]])
}

# it is being invoked.
# CHECK: module
# CHECK-SAME: tf.versions
# CHECK-SAME: bad_consumers
# CHECK-SAME: min_consumer
# CHECK-SAME: producer

# CHECK: "tf_saved_model.global_tensor"() <{is_mutable, sym_name = "[[VAR:[a-zA-Z_0-9]+]]", type = tensor<1x3xf32>, value = {{.*}} : tensor<1x3xf32>}> : () -> ()
# CHECK-NOT: session_initializer

# CHECK:      func {{@[a-zA-Z_0-9]+}}(

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)
       {
         data_format = "NHWC",