// CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%[[ARG_TRANSPOSE]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: %[[RELU:[0-9]*]] = "tf.Relu"(%[[TANH]]) {{.*}} tensor<1x8x4x4xf32>
  // CHECK: return %[[RELU]]

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

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

// Computes rectified linear gradients for a Relu operation.
Status ReluGrad(AbstractContext* ctx, AbstractTensorHandle* const gradients,
                AbstractTensorHandle* const features,
                AbstractTensorHandle** backprops, const char* name = nullptr,
                const char* raw_device_name = nullptr);

// Computes rectified linear: `max(features, 0)`.
Status Relu(AbstractContext* ctx, AbstractTensorHandle* const features,

using tensorflow::TF_StatusPtr;

Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Relu(ctx, inputs[0], &outputs[0], "Relu");
}

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {

@composite.Composite(
    'TestNoOp', derived_attrs=['T: numbertype'], outputs=['o1: T'])
def _composite_no_op():
  pass


@Composite(
    'TestCompositeOp',
    inputs=['x: T', 'y: T'],
    attrs=['act: {"", "relu"}', 'trans: bool = true'],
    derived_attrs=['T: numbertype'],
    outputs=['o1: T', 'o2: T'])
def _composite_op(x, y, act, trans):
  return x + act, y + trans


class TFRGenTensorTest(test.TestCase):

  // CHECK: %[[TANH:[0-9]*]] = "tf.Tanh"(%arg0) {{.*}} tensor<1x4x4x8xf32>
  // CHECK: %[[RELU:[0-9]*]] = "tf.Relu"(%[[TANH]]) {{.*}} tensor<1x4x4x8xf32>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[RELU]], %[[RES_PERM]])
  // CHECK: return %[[RES_TRANSPOSE]]

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

// CHECK-NEXT:         outputs: [ 5 ],
// CHECK-NEXT:         builtin_options_type: SVDFOptions,
// CHECK-NEXT:         builtin_options: {
// CHECK-NEXT:           rank: 2,
// CHECK-NEXT:           fused_activation_function: RELU
// CHECK-NEXT:         }
// CHECK-NEXT:       } ],
// CHECK-NEXT:       name: "main"
// CHECK-NEXT:     } ],
// CHECK-NEXT:     description: "MLIR Converted.",
// CHECK-NEXT:     buffers: [ {
// CHECK-EMPTY:

    # output shape: [-1, 28, 28, 32]
    conv1 = gen_mnist_ops.new_conv2d(x, self.weights['f1'], self.biases['b1'],
                                     1, 1, 1, 1, 'SAME', 'RELU')

    # Max pooling. The kernel size spec {ksize} also follows the layout of
    # the data. Here we have a pooling window of 2, and a stride of 2.
    # output shape: [-1, 14, 14, 32]

// CHECK-NEXT:         outputs: [ 5 ],
// CHECK-NEXT:         builtin_options_type: SVDFOptions,
// CHECK-NEXT:         builtin_options: {
// CHECK-NEXT:           rank: 2,
// CHECK-NEXT:           fused_activation_function: RELU
// CHECK-NEXT:         }
// CHECK-NEXT:       } ],
// CHECK-NEXT:       name: "main"
// CHECK-NEXT:     } ],
// CHECK-NEXT:     description: "MLIR Converted.",
// CHECK-NEXT:     buffers: [ {
// CHECK-EMPTY:

        'input_': input_,
        'filter_': filter_,
        'bias': bias,
        'stride_w': 2,
        'stride_h': 2,
        'dilation_w': 1,
        'dilation_h': 1,
        'padding': 'SAME',
        'act': 'RELU'
    }

    self._assertOpAndComposite([input_, filter_, bias],
                               tf.function(gen_mnist_ops.new_conv2d),
                               ops_defs._composite_conv_add_relu, kwargs)

 Conv2DBackpropInput 52
 DivNoNan 1
 Equal 1
 FusedBatchNorm 53
 FusedBatchNormGrad 53
 Identity 2
 MatMul 3
 MaxPool 1
 MaxPoolGrad 1
 Mean 1
 Mul 164
 Pad 1
 ReadVariableOp 646
 Relu 49
 ReluGrad 49
 Reshape 2
 ResourceApplyKerasMomentum 161
 ShapeN 50
 Softmax 1
 SparseSoftmaxCrossEntropyWithLogits 1
 Square 55
 Squeeze 1
 Sub 106
 Sum 57
 Tile 1