I have written a CRNN for a project I$\text{'}$m working on$,$ by piecing together code I know and code$/$concepts from the web. I need to construct a graphical representation of the architecture I've designed, but, I want some help stepping through it and making sure i understand everything and it's correct.

I am including the function that I think has all the info about the model:

import torch.nn as nn

class CRNN$($nn$.$Module$)$:

def $\_\_$init$\_\_($self$)$:

super$($CRNN$,$ self$).\_\_$init$\_\_()$

# Define the convolutional layers

self.conv$\_$layers $=$ nn$.$Sequential$($

nn$.$Conv$2$d$(107,64,$ kernel$\_$size$=3,$ padding$=1),$

nn$.$ReLU$(),$

nn$.$Conv$2$d$(64,64,$ kernel$\_$size$=3,$ padding$=1),$

nn$.$ReLU$(),$

nn$.$Conv$2$d$(64,64,$ kernel$\_$size$=3,$ padding$=1),$

nn$.$ReLU$(),$

# Add more convolutional layers as needed

$)$

# Define the recurrent layers $($LSTM$,$ GRU, or RNN$)$

self.rnn $=$ nn$.$LSTM$(64,64,$ num$\_$layers$=2,$ batch$\_$first$=$True$)$

# Define the final convolutional layer to generate images

self.final$\_$conv $=$ nn$.$Conv$2$d$(64,1,$ kernel$\_$size$=3,$ padding$=1)$

def forward$($self$,$ x$)$:

# Forward pass through convolutional layers

# x $=$ torch.Size$([16,8,224,224])$

x $=$ self.conv$\_$layers$($x$)$ # torch.Size$([16,64,224,224])$

# Reshape for the recurrent layers

batch$\_$size, channels, height, width $=$ x$.$size$()$

x $=$ x$.$view$($batch$\_$size, channels, $-1).$permute$(0,2,1)$ # torch.Size$([16,50176,64])$

# Forward pass through recurrent layers

x$,\_=$ self.rnn$($x$)$ # torch.Size$([16,50176,64])$

# Reshape back for the final convolutional layer

x $=$ x$.$permute$(0,2,1).$view$($batch$\_$size, $-1,$ height, width$)$ # torch.Size$([16,64,224,224])$

# Forward pass through final convolutional layer

x $=$ self.final$\_$conv$($x$)$ # torch.Size$([16,1,224,224])$

return x