auto qcast = b.create<quantfork::QuantizeCastOp>(
          q.getLoc(), q.getOutput().getType(), q.getInput());
      q.getOutput().replaceAllUsesWith(qcast);
      q.erase();
    } else if (auto q = llvm::dyn_cast<ConstOp>(op)) {
      auto value = q.getValue();
      auto type = q.getResult().getType();
      if (arith::ConstantOp::isBuildableWith(value, type)) {
        auto c = b.create<arith::ConstantOp>(q.getLoc(), q.getValue());

		wantErr            bool
	}{
		{
			name:             "returns expected secret summary onto Stdout",
			callPrime:        true,
			wantOutputSecret: "testdata/secretsummary.txt",
		},
		{
			name:    "errors if config dump is not primed",
			wantErr: true,
		},
		{
			name:               "returns expected workload summary onto Stdout",
			callPrime:          true,
			wantOutputWorkload: "testdata/workloadsummary.txt",
		},

     */
    static final int CROSS_PROJECT_CLASS_DEPENDENCY_LEVEL = 2
    /**
     * How many sibling classes are put onto one 'level' in the class dependency tree
     */
    static final int CLASSES_ON_LEVEL = 3
    /**
     * How many sibling project are put onto one 'level' in the project dependency tree
     */
    static final int PROJECTS_ON_LEVEL = 3

    // We can only clone the constant op or const->dequantize combo. The latter
    // case is useful for float16 quantization. Since all ops have been
    // legalized to tflite ops, so we only care about ConstOp or QConstOp or
    // mlir constant op.
    Operation* input_op = operand.getDefiningOp();
    if (input_op == nullptr) return failure();

    Attribute attr;
    if (matchPattern(input_op, m_Constant(&attr))) {

// Checks if the data format is "NDHWC".
def IsDataFormatNDHWC : ConstantAttr<TF_ConvnetDataFormatAttr, "\"NDHWC\"">;

// Checks if the op is constant op.
def IsConstTensor :  Constraint<CPred<"dyn_cast_or_null<TF::ConstOp>($0.getDefiningOp())">>;

// Checks if the element value has a float type.
def IsFloatElementsAttr : ElementsAttrBase<
  CPred<"$_self.isa<ElementsAttr>() && "

}

TEST_F(LegalizationOpConfigTest, ChecksDynamicPadderOps) {
  EXPECT_TRUE(
      IsDynamicPadderOp(TypeID::get<TF::XlaSetDynamicDimensionSizeOp>()));
  EXPECT_FALSE(IsDynamicPadderOp(TypeID::get<TF::ConstOp>()));
}

// This test is kind of odd. We go through all the Tensorflow types and check
// whether they are legalized with MLIR, TF2XLA, or both. Ideally the sets are

        }
      }

      // Replace the arg with a tf.Const op in the function body.
      builder.setInsertionPointToStart(&func.getBody().front());
      auto const_op = builder.create<TF::ConstOp>(global_tensor.getLoc(),
                                                  *global_tensor.getValue());
      args_to_erase.set(val.getArgNumber());
      for (auto read_op : read_variable_ops_to_erase) {

  // +k8s:conversion-gen=false
  // +optional
  optional string serviceAccount = 9;

  // NodeName is a request to schedule this carp onto a specific node. If it is non-empty,
  // the scheduler simply schedules this carp onto that node, assuming that it fits resource
  // requirements.
  // +optional
  optional string nodeName = 10;

// the init function for a coverage-instrumented package executes, it
// will make a call into the runtime which will create a covMetaBlob
// object for the package and chain it onto a global list.
type CovMetaBlob struct {
	P                  *byte
	Len                uint32
	Hash               [16]byte
	PkgPath            string
	PkgID              int

  // +k8s:conversion-gen=false
  // +optional
  optional string serviceAccount = 9;

  // NodeName is a request to schedule this pod onto a specific node. If it is non-empty,
  // the scheduler simply schedules this pod onto that node, assuming that it fits resource
  // requirements.
  // +optional
  optional string nodeName = 10;