if (auto block_arg =
          mlir::dyn_cast<BlockArgument>(read_var_op.getResource())) {
    if (block_arg.getOwner() != replicate_block) return;

    OpBuilder builder(shape_op);
    auto new_shape_op = builder.create<TF::VariableShapeOp>(
        shape_op.getLoc(), shape_op.getType(),
        replicate_op.GetReplicaOperandForBlockArgument(block_arg,
                                                       /*replica=*/0));

  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);

                       op->getOperands(), result_types, op->getAttrs());
  for (int i = 0; i < op->getNumRegions(); ++i) {
    state.addRegion();
  }
  Operation* new_op = builder.create(state);
  for (const auto& indexed_regions : llvm::enumerate(op->getRegions())) {
    Region& region = op->getRegion(indexed_regions.index());
    IRMapping mapping;
    indexed_regions.value().cloneInto(&region, mapping);

  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"});

  // 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());

            }));

    // 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());
  }