code: def test_funhash(
      self, n_out, n_in, n_data, n_weight, n_alpha, sparsity, gc, dc
  ):
      A = np.random.rand(n_data, n_in)
      A[A > sparsity] = 0
      A_coo = coo_matrix(A)
      val, key, seg = A_coo.data, A_coo.col, A_coo.row
  
      weight = np.random.rand(n_weight).astype(np.float32)
      alpha = np.random.rand(n_alpha).astype(np.float32)
      val = val.astype(np.float32)
      key = key.astype(np.int64)
      seg = seg.astype(np.int32)
  
      op = core.CreateOperator(
          "SparseFunHash",
          ["val", "key", "seg", "weight", "alpha"],
          ["out"],
          num_outputs=n_out,
      )
  
      # Gradient check wrt weight
      self.assertGradientChecks(
          gc, op, [val, key, seg, weight, alpha], 3, [0]
      )
      # Gradient check wrt alpha
      self.assertGradientChecks(
          gc, op, [val, key, seg, weight, alpha], 4, [0]
      )
  
      op2 = core.CreateOperator(
          "SparseFunHash",
          ["val", "key", "seg", "weight"],
          ["out"],
          num_outputs=n_out,
      )
  
      # Gradient check wrt weight
      self.assertGradientChecks(
          gc, op2, [val, key, seg, weight], 3, [0]
      )
  
file_path: pytorch/caffe2/experiments/python/sparse_funhash_op_test.py
imports: import numpy
  from scipy.sparse import coo_matrix
  from hypothesis import given
  import hypothesis.strategies
  from caffe2.python import core
  import caffe2.python.hypothesis_test_util
label_desc: Use image node_id 1 for calling the TestFunHash obj's underlying member method code with example usage: obj.test_funhash(n_out, n_in, n_data, n_weight, n_alpha, sparsity, gc, dc) without return types