Interface interface) {
  auto valid_operand = [&](int64_t idx) { return idx < op->getNumOperands(); };
  if (!llvm::all_of(interface.GetLayoutDependentArgs(), valid_operand)) {
    return op->emitOpError("layout dependent argument index is out of bound");
  }

  auto valid_result = [&](int64_t idx) { return idx < op->getNumResults(); };

		}
	} else {
		result.Memory.Time = metav1.NewTime(time.Unix(0, time.Now().UnixNano()))
		result.Memory.WorkingSetBytes = uint64Ptr(0)
		result.Memory.AvailableBytes = uint64Ptr(0)
		result.Memory.PageFaults = uint64Ptr(0)
	}
	if stats.WritableLayer != nil {
		result.Rootfs.Time = metav1.NewTime(time.Unix(0, stats.WritableLayer.Timestamp))
		if stats.WritableLayer.UsedBytes != nil {

}

// Helper struct holding a resource handle value and unique id associated to the
// resource handle.
struct ResourceHandleValueAndId {
  ResourceHandleValueAndId(Value value, int64_t id) : value(value), id(id) {}

  Value value;
  int64_t id = -1;
};

//===----------------------------------------------------------------------===//
// TF op helper functions for handling resource handles and ids.

        return true;
      }
    }
  }
  return false;
}

}  // namespace

constexpr int64_t ResourceAliasAnalysisInfo::kUnknownResourceId;

void IncrementResourceTypeId(int64_t& resource_type_id) {
  if (resource_type_id == ResourceAliasAnalysisInfo::kMaxResourceTypeId) {
    // We don't expect this to happen, currently there are 10 resource types in

    return absl::OkStatus();
  }

  DialectRegistry registry_;
  MLIRContext context_;
  OwningOpRef<mlir::ModuleOp> mlir_module_;
};

TEST_F(TensorflowDialectToExecutorTest, ConvertsToExecutor) {
  CellReader<int64_t> compilation_status(kExportStreamzName);

  TF_ASSERT_OK(CreateMlirModule("empty_func.mlir"));

  TF_EXPECT_OK(ExportFromTensorflowDialectToExecutor(*mlir_module_));

#ifndef TENSORFLOW_C_TF_FILE_STATISTICS_H_
#define TENSORFLOW_C_TF_FILE_STATISTICS_H_

#include <stdint.h>

typedef struct TF_FileStatistics {
  // The length of the file in bytes.
  int64_t length;
  // The last modified time in nanoseconds.
  int64_t mtime_nsec;
  // Whether the name refers to a directory.
  bool is_directory;
} TF_FileStatistics;

// TODO(b/139060984): `tensorflow::FileStatistics` from

  }
}

void TestEncodeDecode(int line, const std::vector<string>& data) {
  const int64_t n = data.size();
  Status status;
  for (const std::vector<int64_t>& dims :
       std::vector<std::vector<int64_t>>{{n}, {1, n}, {n, 1}, {n / 2, 2}}) {
    // Create C++ Tensor
    Tensor src(tensorflow::DT_STRING, TensorShape(dims));
    for (int64_t i = 0; i < src.NumElements(); ++i) {
      src.flat<tstring>()(i) = data[i];
    }

                             TF_DataType dtype, const int64_t* dims,
                             int num_dims, size_t len, TF_Status* status) {
  TF_SetStatus(status, TF_OK, "");
  auto* cc_ctx = reinterpret_cast<::tensorflow::OpKernelContext*>(context);
  tensorflow::gtl::ArraySlice<const int64_t> dimarray(
      reinterpret_cast<const int64_t*>(dims), num_dims);
  tensorflow::Tensor* tensor;

}

ConstantOp GetScalarConstOfType(Type ty, Location loc, int64_t raw_value,
                                OpBuilder* builder);

ConstantOp GetScalarNegZeroOfType(Type ty, Location loc, OpBuilder* builder);

// Converts an ArrayAttr to a 1D 64-bit dense elements attribute.
DenseIntElementsAttr GetI64ElementsAttr(ArrayAttr attr);
DenseIntElementsAttr GetI64ElementsAttr(llvm::ArrayRef<int64_t> values,

 private:
  ParallelTensor(const ParallelDevice& device,
                 std::vector<TensorHandlePtr> tensors,
                 absl::Span<const int64_t> shape, const TF_DataType dtype)
      : device_(device),
        tensors_(std::move(tensors)),
        shape_(std::vector<int64_t>(shape.begin(), shape.end())),
        dtype_(dtype) {}
  ParallelTensor(const ParallelDevice& device,