code: def _test(self, prefix, input_strategy, refs, gpu=False, **kwargs):
      tester = self
      operator_args = kwargs.pop("operator_args", {})
      threshold = kwargs.pop("threshold", 1e-4)
      grad_check = kwargs.pop("grad_check", True)
  
      @given(X=input_strategy, **hu.gcs)
      def test_segment_ops(self, X, gc, dc):
          if not gpu and gc.device_type > 0:
              return
          for op_name, ref, grad_ref in refs:
              inputs = ["input%d" % i for i in range(0, len(X))]
              op = core.CreateOperator(
                  prefix + op_name, inputs, ["output"], **operator_args
              )
              print("Operator %s, " % op.type, gc.device_type)
  
              def seg_reduce(data, *args):
                  indices, segments = (
                      args if len(args) == 2 else (None, args[0])
                  )
                  out = tester.segment_reduce_op(
                      data=data,
                      segment_ids=segments,
                      indices=indices,
                      reducer=ref,
                  )
                  return (out,)
  
              def seg_reduce_grad(grad_out, outputs, inputs):
                  data = inputs[0]
                  args = inputs[1:]
                  indices, segments = (
                      args if len(args) == 2 else (None, args[0])
                  )
                  # grad r.t. data
                  grad_val = tester.segment_reduce_grad_op(
                      data,
                      segments,
                      grad_ref,
                      grad_out,
                      outputs[0],
                      indices,
                  )
                  # if sparse, include indices along with data gradient
                  data_grad_slice = (
                      (grad_val, indices)
                      if indices is not None
                      else grad_val
                  )
                  # other inputs don't have gradient
                  return (data_grad_slice,) + (None,) * (
                      len(inputs) - 1
                  )
  
              kwargs = {}
              if grad_check:
                  kwargs["output_to_grad"] = "output"
                  kwargs["grad_reference"] = seg_reduce_grad
              self.assertReferenceChecks(
                  device_option=gc,
                  op=op,
                  inputs=X,
                  reference=seg_reduce,
                  threshold=threshold,
                  **kwargs,
              )
  
      return test_segment_ops
  
imports: from functools import partial
  from hypothesis import given, settings
  import numpy
  import unittest
  import hypothesis.strategies
  from caffe2.python import core, workspace
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
label_desc: Use image node_id 3 for calling the TesterBase obj's underlying member method code with example usage: obj._test(prefix, input_strategy, refs, gpu) and returns: test_segment_ops, out, unknown