// 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>

  } : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
  %4 = "tf.BiasAdd"(%3, %cst) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
  %5 = "tf.Relu"(%4) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>


  %6 = "tf.Conv2D"(%arg0, %arg1) {
    data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [],

// CHECK-SAME: f = @composite_conv3d_fn_1}>
// CHECK-NOT: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[RELU:.*]] = "tf.Relu"(%[[PARTITIONEDCALL_0]])
// CHECK: return %[[RELU]]

// CHECK-LABEL: private @composite_conv3d_fn_1

// WEIGHTONLY-DAG: %[[CST:.*]] = "tf.Const"() {{.*}} : () -> tensor<2x3x3x3x2xf32>
// WEIGHTONLY: %[[PARTITIONEDCALL_0:.*]] = "tf.PartitionedCall"(%arg0, %[[CST]])

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) {

    parameters[
      {"quantized_ops": ["${main_op}", "BiasAdd"], "act_func": "internal_requantize_no_activation_fn", "output_type": "!tf_type.qint8"},
      {"quantized_ops": ["${main_op}", "BiasAdd", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "!tf_type.qint8"},
      {"quantized_ops": ["${main_op}", "BiasAdd", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "!tf_type.qint8"},
    ]

    Node* b = ops::UnaryOp("Relu", a, builder.opts().WithName("B"));
    Node* c = ops::UnaryOp("Relu", b, builder.opts().WithName("C"));
    Node* d =
        ops::UnaryOp("UncompilableUnary", c, builder.opts().WithName("D"));
    Node* e = ops::UnaryOp("Relu", d, builder.opts().WithName("E"));
    ops::UnaryOp("Relu", e, builder.opts().WithName("F"));

  // 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:

// 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:

 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