code: def evaluate_with_onnx(
      self,
      x,
      y,
      metrics=["mse"],
      dirname=None,
      multioutput="raw_values",
      batch_size=32,
  ):
      # reshape 1dim input
      x = self._reshape_input(x)
      y = self._reshape_input(y)
  
      yhat = self.predict_with_onnx(
          x, dirname=dirname, batch_size=batch_size
      )
      eval_result = [
          Evaluator.evaluate(
              m, y_true=y, y_pred=yhat, multioutput=multioutput
          )
          for m in metrics
      ]
      return eval_result
  
imports: import torch
  from torch.utils.data import TensorDataset, DataLoader
  import types
  import numpy
  import math
  import pandas
  import tempfile
  import os
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.pytorch_utils import LR_NAME, DEFAULT_LR
label_desc: Use image node_id 21 for calling the PytorchBaseModel obj's underlying member method code with example usage: obj.evaluate_with_onnx(x, y, metrics, dirname, multioutput, batch_size) and returns: eval_result