code: def test_crf_gradient(self, num_tags, num_words):
      base_model = ModelHelper(name="base_model")
      transitions = np.random.randn(num_tags + 2, num_tags + 2).astype(
          np.float32
      )
      predictions = np.random.randn(num_words, 1, num_tags + 2).astype(
          np.float32
      )
      initial = np.random.randn(1, num_tags + 2).astype(np.float32)
      (
          predictions_blob,
          transitions_blob,
          initial_blob,
      ) = base_model.net.AddExternalInputs(
          "predictions_blob", "crf_transitions", "inital_blob"
      )
  
      workspace.FeedBlob(str(predictions_blob), predictions)
      workspace.FeedBlob(str(transitions_blob), transitions)
      workspace.FeedBlob(str(initial_blob), initial)
  
      crf_layer = crf.CRFWithLoss(
          base_model, num_tags, transitions_blob
      )
      crf_layer.build_crf_net(
          predictions_blob, initial_blob, transitions_blob
      )
      op = base_model.net._net.op[-1]
      workspace.RunNetOnce(base_model.param_init_net)
      gradients_to_check = (
          index
          for (index, input_name) in enumerate(op.input)
          if input_name != "crf_net/zero_segment_id"
      )
  
      inputs = [workspace.FetchBlob(name) for name in op.input]
      for param in gradients_to_check:
          self.assertGradientChecks(
              device_option=hu.cpu_do,
              op=op,
              inputs=inputs,
              outputs_to_check=param,
              outputs_with_grads=[1],
              threshold=0.05,
              stepsize=0.001,
          )
  
file_path: pytorch/caffe2/python/operator_test/crf_test.py
imports: from caffe2.python import workspace, crf, brew
  from caffe2.python.model_helper import ModelHelper
  import numpy
  from scipy.special import logsumexp
  import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  from hypothesis import given, settings
label_desc: Use image node_id 2 for calling the TestCRFOp obj's underlying member method code with example usage: obj.test_crf_gradient(num_tags, num_words) without return types