code: def TestOneInput(input_bytes):
      """Test randomized integer fuzzing input for tf.raw_ops.SparseCountSparseOutput."""
      fh = FuzzingHelper(input_bytes)
  
      shape1 = fh.get_int_list(
          min_length=0, max_length=8, min_int=0, max_int=8
      )
      shape2 = fh.get_int_list(
          min_length=0, max_length=8, min_int=0, max_int=8
      )
      shape3 = fh.get_int_list(
          min_length=0, max_length=8, min_int=0, max_int=8
      )
      shape4 = fh.get_int_list(
          min_length=0, max_length=8, min_int=0, max_int=8
      )
  
      seed = fh.get_int()
      indices = tf.random.uniform(
          shape=shape1, minval=0, maxval=1000, dtype=tf.int64, seed=seed
      )
      values = tf.random.uniform(
          shape=shape2, minval=0, maxval=1000, dtype=tf.int64, seed=seed
      )
      dense_shape = tf.random.uniform(
          shape=shape3, minval=0, maxval=1000, dtype=tf.int64, seed=seed
      )
      weights = tf.random.uniform(
          shape=shape4, minval=0, maxval=1000, dtype=tf.int64, seed=seed
      )
  
      binary_output = fh.get_bool()
      minlength = fh.get_int()
      maxlength = fh.get_int()
      name = fh.get_string()
      try:
          (
              _,
              _,
              _,
          ) = tf.raw_ops.SparseCountSparseOutput(
              indices=indices,
              values=values,
              dense_shape=dense_shape,
              weights=weights,
              binary_output=binary_output,
              minlength=minlength,
              maxlength=maxlength,
              name=name,
          )
      except tf.errors.InvalidArgumentError:
          pass
  
label_desc: Use image node_id 1 for calling a global function with example usage: TestOneInput(input_bytes) without return types