original_docstring: Reconstruct the latent regressor sequence given data.
docstring: Reconstruct the latent regressor sequence given data.
docstring_tokens: Reconstruct the latent regressor sequence given data .
code: def _reconstruct_regressor_seq(self, data, ind, param):
          "Reconstruct the latent regressor sequence given data."
          with torch.no_grad():
              # Encode seq.
              z_loc = self.encoder(data[ind][0])[0]
              # Reconstruct
              decoded = self.decoder(
                  z_loc, param("W_q_mn"), param("B_q_mn"), param("inverse_temp_q_mn")
              )
              return torch.exp(decoded["precursor_seq_logits"])
code_tokens: def _reconstruct_regressor_seq ( self , data , ind , param ) : with torch . no_grad ( ) : z_loc = self . encoder ( data [ ind ] [ 0 ] ) [ 0 ] decoded = self . decoder ( z_loc , param ( "W_q_mn" ) , param ( "B_q_mn" ) , param ( "inverse_temp_q_mn" ) ) return torch . exp ( decoded [ "precursor_seq_logits" ] )
short_docstring: Reconstruct the latent regressor sequence given data.
short_docstring_tokens: Reconstruct the latent regressor sequence given data .
comment: "Reconstruct the latent regressor sequence given data."$SEP$# Encode seq.$SEP$# Reconstruct