code: def validate(config, model, valid_ld, metrics, validate_batches):
      import torch
      from bigdl.orca.learn.metrics import Metric
  
      model.eval()
      metrics = Metric.convert_metrics_dict(metrics, backend="pytorch")
      valid_iter = iter(valid_ld)
      with torch.no_grad():
          for j in range(validate_batches):
              # Iterate again from the beginning if running out of batches.
              if j > 0 and j % len(valid_ld) == 0:
                  valid_iter = iter(valid_ld)
              x, y = next(valid_iter)
              o = model(x, y)
              for metric in metrics.values():
                  metric(o, y)
      result = {
          name: metric.compute() for name, metric in metrics.items()
      }
      output = "Validation results: "
      for metric, value in result.items():
          output += "{}:{} ".format(metric, value)
      print(output)
      return result
  
imports: import os
  import subprocess
  import types
  import cloudpickle
  from pyspark.rdd import RDD
  from pyspark.sql import DataFrame
  from torch.utils.data import Dataset, DataLoader
  from bigdl.dllib.utils.log4Error import *
  from bigdl.dllib.utils.utils import get_node_ip
  from bigdl.orca.learn.mpi.mpi_runner import MPIRunner
  from bigdl.orca.learn.mpi.utils import *
label_desc: Use image node_id 5 for calling a global function with example usage: validate(config, model, valid_ld, metrics, validate_batches) and returns: result