Value GetNumElements(TF::TensorListReserveOp op, ValueRange operands,
                       PatternRewriter *rewriter) const override {
    Value scalar_zero = CreateI32SplatConst(op.getLoc(), rewriter, {}, 0);
    Type shape_dtype = getElementTypeOrSelf(op.getElementShape().getType());
    Value num_elements = operands[1];
    IntegerAttr attr;
    if (matchPattern(num_elements, m_Constant(&attr))) {

    }

    auto fused_loc = rewriter.getFusedLoc(locations);

    // The fused contraction has the same operands as the original contraction
    // with `bias` from the BiasAddOp appended.
    SmallVector<Value, 4> operands(contraction.operand_begin(),
                                   contraction.operand_end());
    operands.push_back(bias_add.getBias());

    // The fused contraction has the same attributes as the original

        decomposed_partitioned_call_callees) {
  for (OpOperand& operand : op.getOpOperands()) {
    if (getElementTypeOrSelf(operand.get().getType()).isa<TF::ResourceType>()) {
      return op.emitOpError()
             << "found unexpected type " << operand.get().getType()
             << " of operand #" << operand.getOperandNumber()
             << ", resource type operands are expected to have been "

      // original If op.
      if (op_to_inline.hasTrait<OpTrait::IsTerminator>()) {
        updated_results.reserve(op_to_inline.getNumOperands());
        for (Value operand : op_to_inline.getOperands())
          updated_results.push_back(mapper.lookup(operand));
        break;
      }

      // Otherwise, clone the op here.
      rewriter.clone(op_to_inline, mapper);
    }
    rewriter.replaceOp(op, updated_results);

				s.Insert(randomStringMaker.makeString(here.minStringLen, here.maxStringLen))
			}
			return s
		}
		operands := make([]String, 500)
		for i := range operands {
			operands[i] = makeSet()
		}
		randOperand := func() String { return operands[rand.Intn(len(operands))] }

		b.Run(fmt.Sprintf("insert-%v", here.size), func(b *testing.B) {
			b.ReportAllocs()
			for i := 0; i < b.N; i++ {

  bool ForwardThroughTFOperation(Operation *op,
                                 ArrayRef<const StateT *> operands,
                                 ArrayRef<StateT *> results) {
    if (auto cast = dyn_cast<TF::CastOp>(op)) {
      this->join(results[0], *operands[0]);
    } else if (auto while_op = dyn_cast<TF::WhileRegionOp>(op)) {
      for (auto &region : while_op->getRegions()) {

// immutable because they are from quantization-aware training.
//
// The algorithm traverses each op and sets the quantization parameters of its
// operands and results, according to its quantization specification, and then
// adds the operands and results to the worklist. If there are any conflicts
// (for example, there are quantization parameters propagated from the previous

    }
    // op has implict arguments not listed in operands.
    // Fact: if every op's operands are defined in the same block as op,
    //       then no operation has implicit arugments (constant doesn't count).
    for (auto operand : op->getOperands()) {
      if (mlir::dyn_cast_or_null<BlockArgument>(operand)) continue;
      auto operand_op = operand.getDefiningOp();
      if (IsConstant(operand_op)) continue;

OpFoldResult TFR::EqualOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  assert(operands.size() == 2 && "equal op has two operands");
  auto ctx = getContext();
  if (operands[0] == operands[1]) return BoolAttr::get(ctx, true);
  return BoolAttr::get(ctx, false);
}

OpFoldResult ConstOp::fold(FoldAdaptor adaptor) {
  auto operands = adaptor.getOperands();
  (void)operands;

// CHECK: return %[[identity]] : tensor<1x1x2xf32>

// Tests that an AddV2 op accepting two bf16 operands is transformed into
// an AddV2 op that accepts two fp32 operands.
func.func @cast_bf16_add_v2_to_fp32(%arg0: tensor<2xbf16>, %arg1: tensor<2xbf16>) -> tensor<2xf32> {
  %0 = "tf.AddV2"(%arg0, %arg1) : (tensor<2xbf16>, tensor<2xbf16>) -> tensor<2xbf16>