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))) {

      if (mlir::isa<NoneType>(operand.getType()))
        operands.push_back(kTfLiteOptionalTensor);
      else if (auto stats_op =
                   llvm::dyn_cast_or_null<mlir::quantfork::StatisticsOp>(
                       operand.getDefiningOp()))
        operands.push_back(tensor_index_map.lookup(stats_op.getArg()));
      else
        operands.push_back(tensor_index_map.lookup(operand));
    }

    for (OpOperand& operand : op->getOpOperands()) {
      if (orig_to_new.count(operand.get())) {
        operand.assign(orig_to_new[operand.get()]);
      }
    }
    return WalkResult::advance();
  });

  return success();
}

void XlaBroadcast::runOnOperation() {
  FuncOp func = getOperation();
  mlir::ModuleOp module = func->getParentOfType<mlir::ModuleOp>();
  if (!module) return signalPassFailure();

//
// All HLO infeed ops expect a HLO token type operand and produce a tuple
// containing a token. This HLO token type is used to order multiple infeed
// operations within a computation. The token type can come from other
// infeed/outfeed/send/recv ops or can be generated using create_token op with
// no operands. Here we emit a create_token op to generate the token type

    private fun getExpectedTypeOfElvisOperand(expression: PsiElement): KaType? {
        val binaryExpression = expression.unwrapQualified<KtBinaryExpression> { binaryExpression, operand ->
            binaryExpression.operationToken == KtTokens.ELVIS && (operand == binaryExpression.left || operand == binaryExpression.right)
        } ?: return null
        if (expression !is KtExpression) return null

	}

	tests := []struct {
		name       string
		operations []operation
		operands   []*framework.QueuedPodInfo
		expected   []*framework.QueuedPodInfo
	}{
		{
			name: "add two pod to activeQ and sort them by the timestamp",
			operations: []operation{
				addPodActiveQ,
				addPodActiveQ,
			},
			operands: []*framework.QueuedPodInfo{pInfo2, pInfo1},

  import_config.control_outputs = *control_ret_node_names;
  import_config.upgrade_legacy = true;
  // Disable shape inference during import as some TensorFlow op fails during
  // shape inference with dynamic shaped operands. This in turn causes the
  // import to fail. Shape inference during import is going to be removed and
  // the shape inference pass is run early in the pass pipeline, shape inference
  // during import is not necessary.

*Match* means the axis must match when adding, regarding the broadcasting.
i.e. For both operands `lhs` and `rhs`,
if `operand.quantization_axis` >= 0 and `output.quantization_axis` >= 0,
`operand.dims` - `operand.quantization_axis` must be equal to `output.dims` - `output.quantization_axis`.
  }];

  let arguments = (ins

      llvm::cl::desc("Force data format for all layout sensitive ops")};
  Option<bool> skip_fold_transpose_in_ops{
      *this, "skip-fold-transpose-in-ops",
      llvm::cl::desc("Skip folding transpose operands in Ops which can support "
                     "different layouts.")};
};

// Layout optimization assigns optimal data layout for layout sensitive
// operations, and cancels all redundant transposes.

		}
		if panicAtUnificationDepthLimit {
			panic("unification reached recursion depth limit")
		}
		return false
	}

	// Unification is symmetric, so we can swap the operands.
	// Ensure that if we have at least one
	// - defined type, make sure one is in y
	// - type parameter recorded with u, make sure one is in x
	if asNamed(x) != nil || u.asBoundTypeParam(y) != nil {