code: def _check_input_symbols(self, X):
          
          symbols = np.concatenate(X)
          if (len(symbols) == 1                                # not enough data
              or not np.issubdtype(symbols.dtype, np.integer)  # not an integer
              or (symbols < 0).any()):                         # not positive
              return False
          u = np.unique(symbols)
          return u[0] == 0 and u[-1] == len(u) - 1
docs: Check if ``X`` is a sample from a Multinomial distribution.
  
          That is ``X`` should be an array of non-negative integers from
          range ``[min(X), max(X)]``, such that each integer from the range
          occurs in ``X`` at least once.
  
          For example ``[0, 0, 2, 1, 3, 1, 1]`` is a valid sample from a
          Multinomial distribution, while ``[0, 0, 3, 5, 10]`` is not.