// license that can be found in the LICENSE file.

// Test case for go.dev/issue/44504: panic due to duplicate resolution of slice/index
// operands. We should not try to resolve a LHS expression with invalid syntax.

package p

func _() {
  var items []bool
  items[] /* ERROR "operand" */ = false

namespace mlir {
namespace quant {

// Applies constant folding recursively if the operation and all of its operands
// are foldable. Returns the constants generated by constant-folding or the
// original operation's outputs if not folded.
SmallVector<Value> ConstantFoldOpIfPossible(Operation* op);

// This pattern tries to constant-fold the quantizable operands of supported
// TF operations.
struct ConstantFoldQuantizableOperands : public RewritePattern {

  // within its purview are mutating in nature.
  void PropagatePotentiallyWrittenWithinUnhandledOp(Operation* op);

  // Given a Region associated with the callee and operands from the
  // corresponding callOp, propagate the potentially written decision to the
  // callOp's operands, if the corresponding region's arguments are potentially
  // written resources.
  void PropagatePotentiallyWrittenUpFromCallee(

result is the receiver (usually named z in that case; see below); if it
is one of the operands x or y it may be safely overwritten (and its memory
reused).

Arithmetic expressions are typically written as a sequence of individual
method calls, with each call corresponding to an operation. The receiver
denotes the result and the method arguments are the operation's operands.
For instance, given three *Int values a, b and c, the invocation

	c.Add(a, b)

// and have at least one operand, result type can be inferred using the first
// operand's type.

#define INFER_RETURN_TYPE_COMPONENTS_FROM_OPERANDS(Op)                \
  LogicalResult Op::inferReturnTypeComponents(                        \
      MLIRContext* context, std::optional<Location> location,         \
      ValueShapeRange operands, DictionaryAttr attributes,            \

#include <vector>

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace TFL {

// Check if the given op has stateful operands and return their stateful
// operand indices.
bool IsStatefulOp(Operation* op, std::vector<int>* stateful_operand_indices);

}  // namespace TFL
}  // namespace mlir

// Returns the RankedTensorType for the given operand. TensorFlow constant ops
// may have non-static shape because the shape is not propagated during constant
// folding. If the defining op for the given operand is a constant op, this
// routine uses the constant op's attribute to get the actual shape.
RankedTensorType GetRankedTensorTypeForOperand(Value operand) {
  DenseElementsAttr attr;
  if (matchPattern(operand, m_Constant(&attr))) {

      // 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()) {