src/transformers/models/reformer/modeling_reformer.py/ReformerEncoder/__init__
class ReformerEncoder:    def __init__(self, config):
        super().__init__()
        self.dropout = config.hidden_dropout_prob

        self.layers = nn.ModuleList([ReformerLayer(config, i) for i in range(config.num_hidden_layers)])
        # Reformer is using Rev Nets, thus last layer outputs are concatenated and
        # Layer Norm is done over 2 * hidden_size
        self.layer_norm = nn.LayerNorm(2 * config.hidden_size, eps=config.layer_norm_eps)