class_docstr: Module to be used for image classification purposes.
  
  The module subclasses :py:class:`~kornia.x.Trainer` and overrides the
  :py:func:`~kornia.x.Trainer.evaluate` function implementing a standard
  :py:func:`~kornia.metrics.accuracy` topk@[1, 5].
  
  .. seealso::
      Learn how to use this class in the following
      `example <https://github.com/kornia/tutorials/tree/master/scripts/training/image_classifier/>`__.
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code: def compute_metrics(self, *args: Tensor) -> Dict[str, float]:
      if len(args) != 2:
          raise AssertionError
      out, target = args
      acc1, acc5 = accuracy(out, target, topk=(1, 5))
      return {"top1": acc1.item(), "top5": acc5.item()}
  
file_path: kornia/kornia/x/trainers.py
imports: from typing import Any, Callable, Dict, Optional, Tuple
  from torch.optim import Optimizer, lr_scheduler
  from torch.utils.data import DataLoader
  from kornia.core import Module, Tensor, stack
  from kornia.metrics import accuracy, mean_average_precision, mean_iou
  from .trainer import Trainer
  from .utils import Configuration
label_desc: Use image node_id 1 for calling the ImageClassifierTrainer obj's underlying member method code with example usage: obj.compute_metrics() and returns: dict