code: def test_sparse_input_col_reduce_count(self, dtype):
      num_rows = 128
      num_cols = 27
      size = 100
      np.random.seed(42)
      inp = np.random.randint(
          0, size, (num_rows, num_cols), dtype=dtype
      )
      np_out = np.reshape(
          np.concatenate(
              [
                  np.bincount(inp[j, :], minlength=size)
                  for j in range(num_rows)
              ],
              axis=0,
          ),
          (num_rows, size),
      )
      # from_dense will filter out 0s.
      inp = inp + 1
      # from_dense will cause OOM in GPU.
      with ops.device("/CPU:0"):
          inp_sparse = sparse_ops.from_dense(inp)
          inp_sparse = sparse_tensor.SparseTensor(
              inp_sparse.indices,
              inp_sparse.values - 1,
              inp_sparse.dense_shape,
          )
      self.assertAllEqual(
          np_out,
          self.evaluate(bincount_ops.bincount(arr=inp_sparse, axis=-1)),
      )
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.framework import config
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import bincount_ops
  from tensorflow.python.ops import gen_count_ops
  from tensorflow.python.ops import sparse_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 4 for calling the TestDenseBincount obj's underlying member method code with example usage: obj.test_sparse_input_col_reduce_count(dtype) without return types