code: def run_pipeline_test(self, feature_extractor, examples):
      outputs = feature_extractor("This is a test")
  
      shape = self.get_shape(outputs)
      self.assertEqual(shape[0], 1)
  
      # If we send too small input
      # there's a bug within FunnelModel (output with shape [1, 4, 2, 1] doesn't match the broadcast shape [1, 4, 2, 2])
      outputs = feature_extractor(
          ["This is a test", "Another longer test"]
      )
      shape = self.get_shape(outputs)
      self.assertEqual(shape[0], 2)
  
      outputs = feature_extractor(
          "This is a test" * 100, truncation=True
      )
      shape = self.get_shape(outputs)
      self.assertEqual(shape[0], 1)
  
file_path: transformers/tests/pipelines/test_pipelines_feature_extraction.py
imports: import unittest
  import numpy
  from transformers import FEATURE_EXTRACTOR_MAPPING, IMAGE_PROCESSOR_MAPPING, MODEL_MAPPING, TF_MODEL_MAPPING, FeatureExtractionPipeline, LxmertConfig, is_tf_available, is_torch_available, pipeline
  from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
label_desc: Use image node_id 9 for calling the FeatureExtractionPipelineTests obj's underlying member method code with example usage: obj.run_pipeline_test(feature_extractor, examples) without return types