Region &entry = func.getRegion();
  TypeConverter::SignatureConversion signature_conversion(
      entry.getNumArguments());
  for (const BlockArgument &arg : entry.getArguments()) {
    signature_conversion.addInputs(arg.getArgNumber(),
                                   updated_argument_types[arg.getArgNumber()]);
  }
  rewriter.applySignatureConversion(&entry.front(), signature_conversion);
}

  TF_RETURN_IF_ERROR(assign_op->Reset("AssignVariableOp", nullptr));
  TF_RETURN_IF_ERROR(assign_op->SetAttrType("dtype", dtype));
  TF_RETURN_IF_ERROR(assign_op->AddInput(variable_handle));
  TF_RETURN_IF_ERROR(assign_op->AddInput(value));

  int num_retvals = 0;
  TF_RETURN_IF_ERROR(assign_op->Execute({}, &num_retvals));
  return Status();
}

Status ReadVariable(ImmediateExecutionContext* ctx,

ArgView::ArgView(ArgSpec arg) : arg_(arg) {}

string ArgView::VariableName() const { return arg_.name(); }

string ArgView::SetterMethod() const {
  if (IsList()) {
    return "AddInputList";
  } else {
    return "AddInput";
  }
}

std::vector<string> ArgView::SetterArgs() const { return {VariableName()}; }

bool ArgView::IsList() const { return arg_.arg_type().is_list(); }

int ArgView::Position() const { return arg_.position(); }

  forward_op_->op_name = op;
  forward_op_->attrs.Reset(op);
  return op_->Reset(op, raw_device_name);
}
Status AddInput(AbstractOperation* op_, AbstractTensorHandle* input,
                ForwardOperation* forward_op_) {
  TF_RETURN_IF_ERROR(op_->AddInput(input));
  forward_op_->inputs.push_back(input);
  return absl::OkStatus();
}
Status AddInputList(AbstractOperation* op_,

  return parent_op_->DeviceName();
}
Status TapeOperation::SetDeviceName(const char* name) {
  return parent_op_->SetDeviceName(name);
}
Status TapeOperation::AddInput(AbstractTensorHandle* input) {
  TF_RETURN_IF_ERROR(parent_op_->AddInput(input));
  forward_op_.inputs.push_back(input);
  return absl::OkStatus();
}
Status TapeOperation::AddInputList(
    absl::Span<AbstractTensorHandle* const> inputs) {

    }

    AbstractOperationPtr fn_op(ctx->CreateOperation());
    TF_RETURN_IF_ERROR(fn_op->Reset(fn_name, /*raw_device_name=*/nullptr));
    for (auto input : inputs) {
      TF_RETURN_IF_ERROR(fn_op->AddInput(input));
    }
    int retvals = outputs.size() - null_indices.size();
    std::vector<AbstractTensorHandle*> fn_outputs(retvals);
    TF_RETURN_IF_ERROR(fn_op->Execute(

  auto* new_op = new EagerOperation(&ctx);
  TF_RETURN_IF_ERROR(
      new_op->Reset(fname.c_str(), orig_op->DeviceName().c_str()));
  for (auto input : orig_op->GetInputs()) {
    TF_RETURN_IF_ERROR(new_op->AddInput(input));
  }
  new_op->MutableAttrs()->CopyAttributes(orig_op->Attrs());
  out_op->reset(new_op);

  LOG_FIRST_N(INFO, 1)
      << "Finish Node Expansion Passes. Rewrite the op to call function: "
      << fname;

  SmallVector<Value, 8> operands_;
  llvm::StringMap<Attribute> attrs_;
  std::unique_ptr<OperationState> state_;
  // This is the index of the next ODS operand that will be added with AddInput
  // or AddInput;
  int current_ods_input_ = 0;
  const tensorflow::OpDef* op_def_ = nullptr;
  const char* op_name_ = nullptr;
  string tf_op_type_;
  // TODO(srbs): Use this.
  string device_name_;
};

  if (isa<TracingOperation>(check_numerics_op.get())) {
    s = dyn_cast<TracingOperation>(check_numerics_op.get())
            ->SetOpName("check_numerics");
    ASSERT_EQ(errors::OK, s.code()) << s.message();
  }
  s = AddInput(check_numerics_op.get(), t.get(), &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  string message = "This is the way!";
  s = SetAttrString(check_numerics_op.get(), "message", message.data(),

             llvm::enumerate(block.getArgumentTypes())) {
          TensorType updated_ty =
              UpdateElementTypeTo(original_ty, element_types[block_idx]);
          signature.addInputs(block_idx, {updated_ty});
        }
        rewriter.applySignatureConversion(&region.front(), signature);
      }
    }

    // Replace all uses of `op` results with the newly created op.