code: def evaluate(model, X_test, Y_test, eval_size, batch_size):
      N_test = X_test.shape[0] if eval_size is None else eval_size
  
      if N_test > X_test.shape[0]:
          raise ValueError(
              "Test size can be no larger than {}".format(
                  X_test.shape[0]
              )
          )
  
      with chx.no_backprop_mode():
          total_loss = chx.array(0, dtype=chx.float32)
          num_correct = chx.array(0, dtype=chx.int64)
          for i in range(0, N_test, batch_size):
              x = X_test[i : min(i + batch_size, N_test)]
              t = Y_test[i : min(i + batch_size, N_test)]
  
              y = model(x)
              total_loss += chx.softmax_cross_entropy(y, t).sum()
              num_correct += (
                  (y.argmax(axis=1).astype(t.dtype) == t)
                  .astype(chx.int32)
                  .sum()
              )
  
      mean_loss = float(total_loss) / N_test
      accuracy = int(num_correct) / N_test
      return mean_loss, accuracy
  
file_path: chainer/chainerx_cc/examples/imagenet_py/train_imagenet.py
imports: import argparse
  import time
  import chainer.iterators
  import numpy
  import chainerx
  from image_dataset import PreprocessedDataset
  import resnet50
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, X_test, Y_test, eval_size, batch_size) and returns: mean_loss,  accuracy