Adding a linear layer might work, but you would need to check how it should be used on its inputs.
As described before, the incoming activation is seen as [batch_size, channels, seq_len] and the linear layer accepts [batch_size, *, nb_features], where the * denotes additional dimensions.
In your case the linear layer will be applied on each channel separately and will thus yield an output of [batch_size, channels, out_features].
This operation would thus transform the temporal dimension to the out_features. I’m not familiar with your use case but you should check if this is indeed what you want.
On the other hand, if you want to keep the temporal dimension and apply the linear layer on each time step, permute the input activation to [batch_size, seq_len, channels] and permute it back before feeding it into the transposed conv layer.