if (can_eliminate(result)) continue;
    new_result_types.push_back(result.getType());
  }

  // Rebuild the new operation with lesser number of results.
  OpBuilder builder(op);
  Operation *new_op = Operation::create(
      op->getLoc(), op->getName(), new_result_types, op->getOperands(),
      op->getAttrs(), op->getPropertiesStorage(), op->getSuccessors(),
      op->getNumRegions());
  builder.insert(new_op);

  return th;
}

TFE_TensorHandle* TestVariable(TFE_Context* ctx, float value,
                               const tensorflow::string& device_name) {
  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);

  EXPECT_TRUE(output_shape.IsIdenticalTo(TensorShape()));
}

TEST(ConvertTypeToTensorTypeTest, ConvertStringTensor) {
  mlir::MLIRContext context;
  RegisterDialects(context);
  mlir::Builder b(&context);

  // Create the sample tensor to convert.
  Tensor tensor(DT_STRING, TensorShape({1, 2, 2, 1}));
  EXPECT_EQ(4, tensor.NumElements());
  auto Tt = tensor.flat<tstring>();
  Tt.setValues({"one", "two", "three", "four"});

#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_saved_model_passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace mlir {
/// Create a pass to convert from the TFExecutor to the TF control dialect.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateTFExecutorToControlDialectConversion();
}  // namespace mlir

namespace tensorflow {
namespace {

  // CHLO first.
  stablehlo::StablehloToHloTypeConverter hlo_converter;
  if (legalize_chlo) {
    chlo::populateChloToHloPatterns(context, &hlo_converter, &patterns);
  }
  // ConstantLike op is convenient to create splat constants, but is
  // canonicalized to plain HLO constant if statically shaped. Add the
  // canonicalization pattern to pattern list to enable multi-hop lowering.

      id_map[i] = j++;
      new_operands.push_back(while_op.getOperand(i));
      new_result_types.push_back(while_op.getResultTypes()[i]);
    }
  }

  auto new_while_op = OpBuilder(while_op).create<WhileOp>(
      while_op.getLoc(), new_result_types, new_operands, while_op->getAttrs());
  new_while_op.getCond().takeBody(while_op.getCond());
  new_while_op.getBody().takeBody(while_op.getBody());

    if (!wrapped_op) return failure();
    OpBuilder builder(parser.getBuilder().getContext());
    builder.setInsertionPointToEnd(&block);
    builder.create<YieldOp>(wrapped_op->getLoc(), wrapped_op->getResults());
    result.location = wrapped_op->getLoc();
  } else if (parser.parseRegion(body)) {
    return failure();
  }

            }));

    // According to the NodeDef proto definition, an attribute name from the
    // input TensorFlow GraphDef shouldn't contain '.'. If it does appear in
    // the attribute from MLIR, it is treated as an attribute from function
    // calls.
    std::vector<string> name_tokens =
        absl::StrSplit(name, '.', absl::SkipEmpty());
    TF_RET_CHECK(name_tokens.size() <= 2);

  if (!s.ok()) tmp_var->Unref();
  OP_REQUIRES_OK(context, s);

  OP_REQUIRES_OK(context, TF_TensorToTensor(tmp_var_tf.get(), &tmp_var->val));
  OP_REQUIRES_OK(context,
                 context->step_container()->Create(rm, unique_name, tmp_var));
  context->set_output_ref(0, &tmp_var->mu, &tmp_var->val);

  if (context->track_allocations()) {
    context->record_persistent_memory_allocation(tmp_var->val.AllocatedBytes());
  }