%13 = "tfl.square"(%6) : (tensor<f32>) -> tensor<f32>

  func.return %7, %8, %9, %10, %11, %12, %13 : tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>, tensor<f32>
}

// CHECK-LABEL: @max_with_neg_f32_max_val
// CHECK-SAME: (%[[ARG0:.+]]: tensor<f32>)
func.func @max_with_neg_f32_max_val(%arg0 : tensor<f32>) -> (tensor<f32>, tensor<f32>) {

// CHECK-LABEL: testDotToDotGeneralVectorVector
func.func @testDotToDotGeneralVectorVector(%arg0: tensor<3072xf32>, %arg1: tensor<3072xf32>) -> tensor<f32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<3072xf32>, tensor<3072xf32>) -> tensor<f32>
  func.return %0 : tensor<f32>

// CHECK:      %[[RES:.*]] = "mhlo.dot_general"(%arg0, %arg1) <{
// CHECK-SAME:   dot_dimension_numbers = #mhlo.dot<
// CHECK-SAME:     lhs_contracting_dimensions = [0],

    func @outside_compilation() -> tensor<f32> {
      %0 = "tf_device.cluster"() ( {
        %1 = "tf.Const"() {_xla_outside_compilation = "0", value = dense<1.0> : tensor<f32>} : () -> (tensor<f32>)
        %2 = "tf.Identity"(%1) {_xla_outside_compilation = "0"} : (tensor<f32>) -> (tensor<f32>)
        %3 = "tf.AddV2"(%1, %2) : (tensor<f32>, tensor<f32>) -> (tensor<f32>)
        tf_device.return %3 : tensor<f32>

  %0 = "tf.XlaAllReduce"(%input, %group_assignment) {reduce_op = "Add", mode = "CrossReplica"} : (tuple<tensor<f32>, tensor<f32>>, tensor<2x1xi32>) -> tuple<tensor<f32>, tensor<f32>>
  func.return %0 : tuple<tensor<f32>, tensor<f32>> 
}


// -----

// CHECK: module attributes

}

func.func @branch0(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

func.func @branch1(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  %0 = "tf.Add" (%arg0, %arg1) {device = "/device:CPU:0"}  : (tensor<f32>, tensor<f32>) -> tensor<f32>

@testUnidirectionalSequenceLstmWithIntermediates(%arg0: tensor<? x ? x ? x f32>, %arg1: tensor<? x ? x f32>, %arg2: tensor<? x ? x f32>, %arg3: tensor<? x ? x f32>, %arg4: tensor<? x ? x f32>, %arg5: tensor<? x ? x f32>, %arg6: tensor<? x ? x f32>, %arg7: tensor<? x ? x f32>, %arg8: tensor<? x ? x f32>, %arg9: tensor<? x f32>, %arg10: tensor<? x f32>, %arg11: tensor<? x f32>, %arg12: tensor<? x f32>, %arg13: tensor<? x f32>, %arg14: tensor<? x f32>, %arg15: tensor<? x f32>, %arg16: tensor<? x ? x f32>, %arg17:...

// WHOLE-PASSES-DAG:  %[[rv2:.*]] = "tfl.read_variable"({{.*}}) : (tensor<*x!tf_type.resource<tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>>>) -> tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>

  func.return %6 : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
}

// DEBUG-LABEL: QuantizeSplit
func.func @QuantizeSplit(%arg: tensor<4x!quant.uniform<u8:f32, 1.0>>, %cst: tensor<i32>) -> (tensor<2x!quant.uniform<u8:f32, 1.0>>,tensor<2x!quant.uniform<u8:f32, 1.0>>) {
  %0 = "tfl.dequantize"(%arg) : (tensor<4x!quant.uniform<u8:f32, 1.0>>) -> tensor<4xf32>

    %val_2 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @f_callee} : (tensor<!tf_type.resource<tensor<f32>>>) -> (tensor<f32>)
    func.return %val_2 : tensor<f32>
  }

  func.func private @f_callee(%arg0: tensor<*x!tf_type.resource>) -> tensor<f32>  {
    %cst_1 = arith.constant dense<2.0> : tensor<f32>
    func.return %cst_1 : tensor<f32>
  }

  "tf_saved_model.global_tensor"() { sym_name = "some_constant", type = tensor<f32>, value = dense<42.0> : tensor<f32> } : () -> ()

  // expected-error@+1 {{all 'tf_saved_model.index_path' arg attributes should precede all 'tf_saved_model.bound_input' arg attributes}}
  func.func @f(
    %arg0: tensor<f32> {tf_saved_model.bound_input = @some_constant},
    %arg1: tensor<f32> {tf_saved_model.index_path = [0]}