code: def test_ensure_clipped(self, X, in_place, sparse, indices, gc, dc):
      if (not in_place) and sparse:
          return
      param = X.astype(np.float32)
      m, n = param.shape
      indices = np.array(np.nonzero(indices)[0], dtype=np.int64)
      grad = np.random.rand(len(indices), n)
      workspace.FeedBlob("indices", indices)
      workspace.FeedBlob("grad", grad)
      workspace.FeedBlob("param", param)
      input = ["param", "indices", "grad"] if sparse else ["param"]
      output = "param" if in_place else "output"
      op = core.CreateOperator("EnsureClipped", input, output, min=0.0)
      workspace.RunOperatorOnce(op)
  
      def ref():
          return (
              np.array(
                  [
                      np.clip(X[i], 0, None) if i in indices else X[i]
                      for i in range(m)
                  ]
              )
              if sparse
              else np.clip(X, 0, None)
          )
  
      npt.assert_allclose(workspace.blobs[output], ref(), rtol=1e-3)
  
file_path: pytorch/caffe2/python/operator_test/ensure_clipped_test.py
imports: import caffe2.python.hypothesis_test_util
  import hypothesis.strategies
  import numpy
  import numpy.testing
  from caffe2.python import core, workspace
  from hypothesis import given
label_desc: Use image node_id 1 for calling the TestEnsureClipped obj's underlying member method code with example usage: obj.test_ensure_clipped(X, in_place, sparse, indices, gc, dc) and returns: unknown