code: def test_query_with_layer_name_that_is_no_embedding_layer(
      self, num_samples=100
  ):
      strategy = ExpectedGradientLengthMaxWord(
          4, "fc", batch_size=100, device="cpu"
      )
  
      embedding_matrix = torch.FloatTensor(
          np.random.rand(num_samples, 5)
      )
      clf = KimCNNClassifier(2, embedding_matrix=embedding_matrix)
      dataset = random_text_classification_dataset(
          num_samples=num_samples
      )
  
      indices_labeled = np.random.choice(
          np.arange(num_samples), size=10, replace=False
      )
      indices_unlabeled = np.array(
          [
              i
              for i in range(len(dataset))
              if i not in set(indices_labeled)
          ]
      )
      clf.fit(dataset[indices_labeled])
  
      y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
  
      with self.assertRaisesRegex(
          ValueError, r"Given parameter \(layer_name=fc\) is not"
      ):
          strategy.query(
              clf, dataset, indices_labeled, indices_unlabeled, y
          )
  
file_path: small-text/tests/integration/small_text/integrations/pytorch/query_strategies/test_query_strategies.py
imports: import unittest
  import pytest
  import numpy
  from unittest import mock
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from tests.integration.small_text.query_strategies.test_query_strategies import QueryStrategiesExhaustiveIntegrationTest
label_desc: Use image node_id 1 for calling the ExpectedGradientLengthMaxWordTest obj's underlying member method code with example usage: obj.test_query_with_layer_name_that_is_no_embedding_layer(num_samples) without return types