use implicit padding when possible (#152)

        for (out_channels, kernel_size) in convolutions:

            self.projections.append(Linear(in_channels, out_channels)

                                    if in_channels != out_channels else None)

            if kernel_size % 2 == 1:

               padding = kernel_size //2

            else:

               padding = 0

            self.convolutions.append(

                ConvTBC(in_channels, out_channels * 2, kernel_size,

                        dropout=dropout)

                        dropout=dropout, padding=padding)

            )

            in_channels = out_channels

        self.fc2 = Linear(in_channels, embed_dim)

        for proj, conv in zip(self.projections, self.convolutions):

            residual = x if proj is None else proj(x)

            x = F.dropout(x, p=self.dropout, training=self.training)

            if conv.kernel_size[0] % 2 == 1:

                # padding is implicit in the conv

                x = conv(x)

            else: 

                padding_l = (conv.kernel_size[0] - 1) // 2

                padding_r = conv.kernel_size[0] // 2

                x = F.pad(x, (0, 0, 0, 0, padding_l, padding_r))