crnn.pytorch | Convolutional recurrent network in pytorch | Machine Learning library

I was trying to port CRNN model to Keras.

But, I got stuck while connecting output of Conv2D layer to LSTM layer.

Output from CNN layer will have a shape of ( batch_size, 512, 1, width_dash) where first one depends on batch_size, and last one depends on input width of input ( this model can accept variable width input )

For eg: an input with shape [2, 1, 32, 829] was resulting output with shape of (2, 512, 1, 208)

Now, as per Pytorch model, we have to do squeeze(2) followed by permute(2, 0, 1) it will result a tensor with shape [208, 2, 512 ]

I was trying to implement this is Keras, but I was not able to do that because, in Keras we can not alter batch_size dimension in a keras.models.Sequential model

Can someone please guide me how to port above part of this model to Keras?

Current state of ported CNN layer

l have a program that l run using anacoda environment. l have anaconda3, python3.5 to run l do the following steps.