code: def single_layer_softmax_classifier(
      train_dataset: datasets.RegressionDataset,
      test_dataset: datasets.RegressionDataset,
      epochs: int,
      num_classes: int,
      optimizer: tf.keras.optimizers.Optimizer,
      loss: Union[
          tf.keras.losses.Loss, str
      ] = "categorical_crossentropy",
      batch_size: int = 32,
      kernel_regularizer: Optional[
          tf.keras.regularizers.Regularizer
      ] = None,
  ) -> Tuple[Any, List[float]]:
      """Trains a single layer neural network classifier with softmax activation.
  
      Args:
        train_dataset: consists of num_train many labeled examples, where the labels
          are in {0,1,...,num_classes-1}.
        test_dataset: consists of num_test many labeled examples, where the labels
          are in {0,1,...,num_classes-1}.
        epochs: the number of epochs.
        num_classes: the number of classes.
        optimizer: a tf.keras optimizer.
        loss: a tf.keras loss function.
        batch_size: a positive integer.
        kernel_regularizer: a regularization function.
  
      Returns:
        List of test accuracies (one for each epoch) on test_dataset of model
        trained on train_dataset.
      """
      one_hot_train_labels = tf.one_hot(
          train_dataset.labels, num_classes
      )
      one_hot_test_labels = tf.one_hot(test_dataset.labels, num_classes)
      model = tf.keras.Sequential()
      model.add(
          tf.keras.layers.Dense(
              units=num_classes,
              activation="softmax",
              kernel_regularizer=kernel_regularizer,
          )
      )
      model.compile(optimizer, loss=loss, metrics=["accuracy"])
      history = model.fit(
          train_dataset.points,
          one_hot_train_labels,
          batch_size=batch_size,
          epochs=epochs,
          validation_data=(test_dataset.points, one_hot_test_labels),
          verbose=0,
      )
      weights = model.layers[0].weights
      return weights, history.history["val_accuracy"]
  
label_desc: Use image node_id 1 for calling a global function with example usage: single_layer_softmax_classifier(train_dataset, test_dataset, epochs, num_classes, optimizer, loss, batch_size, kernel_regularizer) and returns: weights,  history