class_docstr: The ``TemplateSKLearnClassifier`` is a :class:`~flash.core.classification.ClassificationTask` that classifies
  tabular data from scikit-learn.
  
  Args:
      num_features: The number of features (elements) in the input data.
      num_classes: The number of classes (outputs) for this :class:`~flash.core.model.Task`.
      backbone: The backbone name (or a tuple of ``nn.Module``, output size) to use.
      backbone_kwargs: Any additional kwargs to pass to the backbone constructor.
      loss_fn: The loss function to use. If ``None``, a default will be selected by the
          :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
      optimizer: Optimizer to use for training.
      lr_scheduler: The LR scheduler to use during training.
      metrics: Any metrics to use with this :class:`~flash.core.model.Task`. If ``None``, a default will be selected
          by the :class:`~flash.core.classification.ClassificationTask` depending on the ``multi_label`` argument.
      learning_rate: The learning rate for the optimizer.
      multi_label: If ``True``, this will be treated as a multi-label classification problem.
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docstr: For the test step, we just extract the :attr:`~flash.core.data.io.input.DataKeys.INPUT` and
  :attr:`~flash.core.data.io.input.DataKeys.TARGET` keys from the input and forward them to the
  :meth:`~flash.core.model.Task.test_step`.
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code: def test_step(self, batch: Any, batch_idx: int) -> Any:
      """For the test step, we just extract the :attr:`~flash.core.data.io.input.DataKeys.INPUT` and
      :attr:`~flash.core.data.io.input.DataKeys.TARGET` keys from the input and forward them to the
      :meth:`~flash.core.model.Task.test_step`."""
      batch = (batch[DataKeys.INPUT], batch[DataKeys.TARGET])
      return super().test_step(batch, batch_idx)
  
file_path: lightning-flash/src/flash/template/classification/model.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  from torch import Tensor, nn
  from flash.core.classification import ClassificationTask
  from flash.core.data.io.input import DataKeys
  from flash.core.registry import FlashRegistry
  from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
  from flash.template.classification.backbones import TEMPLATE_BACKBONES
label_desc: Use image node_id 4 for calling the TemplateSKLearnClassifier obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: super