docstr: Performs test and computes the accuracy of the model
  
  :param test_batch: Batch data
  :param batch_idx: Batch indices
  
  :return: output - Testing accuracy
docstr_tok: ["Performs","test","and","computes","the","accuracy","of","the","model",":","param","test_batch",":","Batch","data",":","param","batch_idx",":","Batch","indices",":","return",":","output","-","Testing","accuracy"]
code: def test_step(self, test_batch, batch_idx):
      """
      Performs test and computes the accuracy of the model
  
      :param test_batch: Batch data
      :param batch_idx: Batch indices
  
      :return: output - Testing accuracy
      """
      x, y = test_batch
      output = self.forward(x)
      _, y_hat = torch.max(output, dim=1)
      test_acc = accuracy(
          y_hat.cpu(), y.cpu(), task="multiclass", num_classes=10
      )
      self.test_outputs.append(test_acc)
      return {"test_acc": test_acc}
  
imports: import lightning
  import torch
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
  from torchmetrics.functional import accuracy
  from torchvision import datasets, transforms
label_desc: Use image node_id 7 for calling the LightningMNISTClassifier obj's underlying member method code with example usage: obj.test_step(test_batch, batch_idx) and returns: dict