data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();
    if (else_tensor.getRank() > 0)
      data_first_dim = std::max(else_tensor.getShape().front(), data_first_dim);
  } else if (then_has_rank) {
    data_rank = then_tensor.getRank();
    if (then_tensor.getRank() > 0)
      data_first_dim = then_tensor.getShape().front();

  if (!output_ty) return success();

  int64_t expected_output_rank = std::max(x_ty.getRank(), y_ty.getRank());
  if (output_ty.getRank() != expected_output_rank) {
    return op.emitOpError()
           << "found invalid output rank, expected " << expected_output_rank
           << " but got " << output_ty.getRank();
  }

  // Check output batch dim with potential broadcasting.

    auto reshape_input_ty = mlir::dyn_cast<ShapedType>(reshape_input.getType());
    if (!reshape_input_ty.hasStaticShape() || input_ty.getRank() == 0 ||
        reshape_input_ty.getRank() == 0 ||
        input_ty.getDimSize(input_ty.getRank() - 1) !=
            reshape_input_ty.getDimSize(reshape_input_ty.getRank() - 1)) {
      return failure();
    }

    // Connect the input to the one of reshape.

// Converts the condition for an IfOp/WhileOp to a boolean value.
Value ConvertConditionToBoolean(Operation* op, Value cond) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(cond.getType()))
    if (ranked_type.getRank() == 0 &&
        ranked_type.getElementType().isSignlessInteger(1))
      return cond;

  OpBuilder builder(op);
  Value to_bool = builder.create<TF::ToBoolOp>(op->getLoc(), cond);

        }
    }

    private static boolean hasGroovyPropertyRequests(List<CallInterceptionRequest> requests) {
        return requests.stream().anyMatch(it -> it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_GETTER || it.getInterceptedCallable().getKind() == GROOVY_PROPERTY_SETTER);
    }

    @SuppressWarnings("ReturnValueIgnored")
    private static CodeBlock interceptorClassJavadoc(Collection<CallInterceptionRequest> requests) {

        CodeBlock result = interceptedCallable.getKind() == CallableKindInfo.STATIC_METHOD ? CodeBlock.of("opcode == $T.INVOKESTATIC", Opcodes.class) :
            interceptedCallable.getKind() == CallableKindInfo.INSTANCE_METHOD ? CodeBlock.of("(opcode == $1T.INVOKEVIRTUAL || opcode == $1T.INVOKEINTERFACE)", Opcodes.class) :

  for (auto idx : val1_indices) {
    if (idx < 0) idx = idx + val1_shape.getRank();
    if (idx >= val1_shape.getRank() || val1_shape.isDynamicDim(idx)) {
      return false;
    }
    val1_result *= val1_shape.getDimSize(idx);
  }

  for (auto idx : val2_indices) {
    if (idx < 0) idx = idx + val2_shape.getRank();
    if (idx >= val2_shape.getRank() || val2_shape.isDynamicDim(idx)) {
      return false;
    }

        /*parallel_iterations=*/10,
        /*is_stateless=*/false, /*shape_invariant=*/false);
    new_while.getCond().takeBody(while_op.getCond());
    new_while.getBody().takeBody(while_op.getBody());
    ReplaceReturnOp(new_while.getCond(), rewriter);
    ReplaceReturnOp(new_while.getBody(), rewriter);
    rewriter.replaceOp(while_op, new_while.getResults());
    return success();
  }

  return type && type.getRank() == rank &&
         mlir::isa<FloatType>(type.getElementType());
}

// Returns true if the given `value` has the specified rank or has unranked
// type.
inline bool IsOfRankOrUnranked(Value value, int64_t rank) {
  RankedTensorType type = GetRankedTensorTypeForOperand(value);
  return !type || type.getRank() == rank;
}

  auto num_loop_carried = while_op.getCond().getNumArguments();
  auto not_carried_operands =
      while_op.getOperands().drop_front(num_loop_carried);
  extern_values.insert(not_carried_operands.begin(),
                       not_carried_operands.end());
  auto old_extern_values_size = extern_values.size();

  llvm::SmallVector<Region*, 2> regions{&while_op.getCond(),