// CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])
  // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%[[ARG_TRANSPOSE]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: %[[ADD:[0-9]*]] = "tf.AddV2"(%[[TANH]], %[[TANH]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: return %[[ADD]]

  %0 = "tf.Tanh"(%arg0) : (tensor<1x4x4x8xf32>) -> tensor<1x4x4x8xf32>

DATA ·atanhtabh2075<> + 8(SB)/8, $-.5
GLOBL ·atanhtabh2075<> + 0(SB), RODATA, $16

// Atanh returns the inverse hyperbolic tangent of the argument.
//
// Special cases are:
//      Atanh(1) = +Inf
//      Atanh(±0) = ±0
//      Atanh(-1) = -Inf
//      Atanh(x) = NaN if x < -1 or x > 1
//      Atanh(NaN) = NaN
// The algorithm used is minimax polynomial approximation

  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res


@tf.RegisterGradient('NewConv2D')
def _conv_add_relu_grad(op: ops.Operation, grad):
  act = op.get_attr('act')
  y = op.outputs[0]
  if act == 'RELU':
    grad = gen_nn_ops.relu_grad(grad, y)
  elif act == 'RELU6':
    grad = gen_nn_ops.relu6_grad(grad, y)
  elif act == 'TANH':
    y = math_ops.conj(y)

 Const 7
 GreaterEqual 2
 MatMul 1
 Mul 5
 Select 2
 Sigmoid 3
 Snapshot 1
 Split 1
 Tanh 2
cluster 22 size 28
 Add 3
 BiasAdd 1
 Cast 1
 ConcatV2 1
 Const 5
 GreaterEqual 1
 MatMul 1
 Mul 5
 Select 3
 Sigmoid 3
 Snapshot 1
 Split 1
 Tanh 2
cluster 23 size 423
 Add 12
 AddN 28
 BiasAddGrad 6
 BroadcastGradientArgs 12
 Cast 12

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>
  // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%arg0) {{.*}} tensor<1x4x4x8xf32>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[TANH]], %[[RES_PERM]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: return %[[RES_TRANSPOSE]]

  %0 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>

// CHECK-NEXT:         builtin_options_type: SequenceRNNOptions,
// CHECK-NEXT:         builtin_options: {
// CHECK-NEXT:           time_major: true,
// CHECK-NEXT:           fused_activation_function: TANH
// CHECK-NEXT:         }
// CHECK-NEXT:       } ],
// CHECK-NEXT:       name: "main"
// CHECK-NEXT:     } ],
// CHECK-NEXT:     description: "MLIR Converted.",
// CHECK-NEXT:     buffers: [ {
// CHECK-EMPTY:

    %5, %6, %7, %8,
    %9, %9, %9,
    %10, %11,
    %10, %10,
    %9, %9,
    %recurrent_input, %cell_input,
    %9, %9, %9, %9) {
      cell_clip = 1.000000e+01 : f32,
      fused_activation_function = "TANH",
      proj_clip = 0.000000e+00 : f32,
      time_major = false} : (
        tensor<1x2x3xf32>,
        tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,

  // CHECK-NEXT: [[r2:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.Tanh"([[r1]]) {T = f32}
  %4 = "tf.Tanh"(%3) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:CPU:0"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK-NOT: tf.Identity

          (variadic $inputs),
          ConstantStrAttr<StrAttr, "aten.gelu.default">, $attrs, $_, $_),
        (TFL_GeluOp $inputs, ConstBoolAttrTrue),

          "intermediates": [
            25, 26, 27, 28, 29
          ],
          "builtin_options_type": "UnidirectionalSequenceLSTMOptions",
          "builtin_options": {
            "fused_activation_function": "TANH",
            "cell_clip": 50.0
          },
          "mutating_variable_inputs": [
            false,
            false, false, false, false,
            false, false, false, false,