code: def get_possible_initializer_keys(
        cls, use_peepholes=False, use_batch_norm_h=True, use_batch_norm_x=False,
        use_batch_norm_c=False):
      
  
      possible_keys = cls.POSSIBLE_INITIALIZER_KEYS.copy()
      if not use_peepholes:
        possible_keys.difference_update(
            {cls.W_F_DIAG, cls.W_I_DIAG, cls.W_O_DIAG})
      if not use_batch_norm_h:
        possible_keys.remove(cls.GAMMA_H)
      if not use_batch_norm_x:
        possible_keys.remove(cls.GAMMA_X)
      if not use_batch_norm_c:
        possible_keys.difference_update({cls.GAMMA_C, cls.BETA_C})
      return possible_keys
docs: Returns the keys the dictionary of variable initializers may contain.
  
      The set of all possible initializer keys are:
        w_gates:  weight for gates
        b_gates:  bias of gates
        w_f_diag: weight for prev_cell -> forget gate peephole
        w_i_diag: weight for prev_cell -> input gate peephole
        w_o_diag: weight for prev_cell -> output gate peephole
        gamma_h:  batch norm scaling for previous_hidden -> gates
        gamma_x:  batch norm scaling for input -> gates
        gamma_c:  batch norm scaling for cell -> output
        beta_c:   batch norm bias for cell -> output
  
      Args:
        cls:The class.
        use_peepholes: Boolean that indicates whether peephole connections are
          used.
        use_batch_norm_h: Boolean that indicates whether to apply batch
          normalization at the previous_hidden -> gates contribution. If you are
          experimenting with batch norm then this may be the most effective to
          turn on.
        use_batch_norm_x: Boolean that indicates whether to apply batch
          normalization at the input -> gates contribution.
        use_batch_norm_c: Boolean that indicates whether to apply batch
          normalization at the cell -> output contribution.
  
      Returns:
        Set with strings corresponding to the strings that may be passed to the
          constructor.