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class PositionalEncoding(nn.Module):
    def __init__(self,
                 d_model: int,
                #  dropout: float = 0.1,
                #  max_len: int = 5000
                 max_len: int = 52
                 ):
        super(PositionalEncoding, self).__init__()
        
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
            )
        
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        
        pe = pe.unsqueeze(0).transpose(0, 1)
        self.register_buffer('pe', pe)
    
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x