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.

def get_possible_initializer_keys(
      cls, use_peepholes=False, use_batch_norm_h=True, use_batch_norm_x=False,
      use_batch_norm_c=False):
    """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.
    """

    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