func.func private @f(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  "SomeOp"(%arg1) : (tensor<f32>) -> ()
  return %arg1 : tensor<f32>
}

// CHECK-LABEL: removes_first_arg
func.func @removes_first_arg(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
  // CHECK: call{{.*}}(%arg1)
  %1 = func.call @f(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  return %1 : tensor<f32>
}

func.func @test_batch_matmul_to_einsum(%arg0: tensor<1x2x3xf32>, %arg1: tensor<3x4xf32>) -> tensor<1x2x4xf32> {
  // CHECK-LABEL: test_batch_matmul_to_einsum
  // CHECK: "tf.Einsum"(%arg0, %arg1) <{equation = "...mk,...kn->...mn"}> : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>
  %0 = "tf.BatchMatMul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<1x2x3xf32>, tensor<3x4xf32>) -> tensor<1x2x4xf32>

		{name: "ADDF", argLength: 2, reg: fp21, asm: "ADDF", commutative: true},   // arg0 + arg1
		{name: "ADDD", argLength: 2, reg: fp21, asm: "ADDD", commutative: true},   // arg0 + arg1
		{name: "SUBF", argLength: 2, reg: fp21, asm: "SUBF"},                      // arg0 - arg1
		{name: "SUBD", argLength: 2, reg: fp21, asm: "SUBD"},                      // arg0 - arg1
		{name: "MULF", argLength: 2, reg: fp21, asm: "MULF", commutative: true},   // arg0 * arg1

		{name: "I64Ne", asm: "I64Ne", argLength: 2, reg: gp21, typ: "Bool"},   // arg0 != arg1
		{name: "I64LtS", asm: "I64LtS", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 < arg1 (signed)
		{name: "I64LtU", asm: "I64LtU", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 < arg1 (unsigned)
		{name: "I64GtS", asm: "I64GtS", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 > arg1 (signed)

        when:
        logger().error("{}", arg1)

        then:
        singleLogEvent().message("arg1").logLevel(ERROR)

        when:
        logger().log(ERROR, "{}", arg1)

        then:
        singleLogEvent().message("arg1").logLevel(ERROR)

        when:
        logger().error("{} {}", arg1, arg2)

        then:
        singleLogEvent().message("arg1 arg2").logLevel(ERROR)

        when:

  // CHECK-NEXT:  mhlo.minimum %arg0, %arg1 : tensor<4xf32>
  %0 = "tf.Minimum"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

// CHECK-LABEL: func @mod
// CHLO-LABEL: func @mod
func.func @mod(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK-NEXT:  mhlo.remainder %arg0, %arg1 : tensor<4xf32>

  %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",

  %0 = tfl.mul %arg0, %arg1 {fused_activation_function = "NONE"}: tensor<? x complex<f32>>
  func.return %0#0 : tensor<? x complex<f32>>
}

// CHECK-LABEL: testAddWithI64Broadcasting
func.func @testAddWithI64Broadcasting(tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64> {
^bb0(%arg0: tensor<2x3xi64>, %arg1: tensor<3xi64>):
  // CHECK: tfl.add(%arg0, %arg1)

}

func.func @einsum_basic(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> {
  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
  func.return %0 : tensor<3x4x6xf32>
  // CHECK-LABEL: einsum_basic

// CHECK-LABEL: listReserveScalarShapeI32
func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>