I have not used Keras. Is it possible that LSTM_5 is doing something beyond a simply LSTM?
As @SANKALP_SHUBHAM suggest it may simply be taking the output at the last time step.
Indeed, when looking at Keras documentation this seems to be the case

Blockquote
return_sequences: Boolean. Whether to return the last output. in the output sequence, or the full sequence. Default: False.

above from keras LSTM documentation.
if you wanted to replicate the above exactly in pytorch you could do the following either of the following:
output, hidden = yourLSTM()
1)take output[19] (your last output)
2)take hidden
The last hidden state should correspond to the last output as output simply stores the hidden states at each time step.
I am not sure that this is what you should be doing. You would be getting rid of the hidden states of all your previous timesteps. Which in this case would correspond to the previous 19 vectors.
Instead I would suggest taking the output of your LSTM of shape [1, 20,256] = [seq_len, batch_size, input_size] and reshaping it so it is of shape [20, 1, 256]  = [batch_size, seq_length, input_size]. You can use torch.transpose() to do this.
Pass the reshaped data to an nn.Linear() layer with 256 neurons using your activation function of choice (2 things are same as Dense).