this is the full code, modify it to resolve the shape issue. Decoder part cant be modified

def train$\_$classifier$($args$,$ train, dev$)$:

# Initialize the model

model $=$ Transformer$($embed$\_$size$=20,$

num$\_$layers$=1,$

max$\_$length$=100,$

num$\_$classes$=3,$

vocab$\_$size$=27)$ # Adjust input$\_$size and num$\_$classes as needed

# Optimizer and loss function

optimizer $=$ optim.Adam$($model$.$parameters$(),$ lr$=1$e$-4)$

loss$\_$fcn $=$ nn$.$NLLLoss$()$

# Hyperparameters

num$\_$epochs $=10$

batch$\_$size $=32$

# Training loop

for epoch in range$($num$\_$epochs$)$:

total$\_$loss $=0\mathrm{.}0$

random.seed$($epoch$)$

# Shuffle the training data for each epoch

random.shuffle$($train$)$

for idx in range$(0,$ len$($train$),$ batch$\_$size$)$:

batch $=$ train$[$idx:idx $+$ batch$\_$size$]$

batch$\_$loss $=0\mathrm{.}0$

# Collect inputs and outputs for the current batch

input$\_$tensors $=[]$

output$\_$tensors $=[]$

for example in batch:

# Get the input and output tensors from the LetterCountingExample

input$\_$tensor $=$ example.input$\_$tensor.unsqueeze$(0)$ # Add batch dimension

output$\_$tensor $=$ example.output$\_$tensor.unsqueeze$(0)$ # Add batch dimension

input$\_$tensors.append$($input$\_$tensor$)$

output$\_$tensors.append$($output$\_$tensor$)$

# Stack the input tensors to create a batch

input$\_$batch $=$ torch.cat$($input$\_$tensors, dim$=0)$ # Shape: $($batch$\_$size, seq$\_$length$)$

output$\_$batch $=$ torch.cat$($output$\_$tensors, dim$=0)$ # Shape: $($batch$\_$size,$)$

# Forward pass $($get log probabilities and attention maps$)$

log$\_$probs, $\_=$ model$($input$\_$batch$)$ # Ensure model outputs are correct

# Reshape to match the loss function

seq$\_$length $=$ input$\_$batch.size$(1)$ # Get the sequence length

output$\_$batch$\_$expanded $=$ output$\_$batch.unsqueeze$(1).$expand$(-1,$ seq$\_$length$)$ # Expand the output batch

log$\_$probs $=$ log$\_$probs.view$(-1,3)$ # Reshape to $($batch$\_$size $*$ seq$\_$length, num$\_$classes$)$

# Flatten the expanded output to match log$\_$probs

loss $=$ loss$\_$fcn$($log$\_$probs, output$\_$batch$\_$expanded.view$(-1))$ # Reshape to match log$\_$probs

# Backpropagation and optimization step

optimizer.zero$\_$grad$()$ # Clear previous gradients

loss.backward$()$ # Compute gradients

optimizer.step$()$ # Update model parameters

# Accumulate the loss

batch$\_$loss $+=$ loss.item$()$

total$\_$loss $+=$ loss.item$()$

print$($f$"$Batch loss: $\{$batch$\_$loss$\}")$

print$($f$"$Total loss on epoch $\{$epoch $+1\}$: $\{$total$\_$loss$\}")$

# Set the model to evaluation mode after training

model.eval$()$

return model

####################################

# DO NOT MODIFY IN YOUR SUBMISSION #

####################################

def decode$($model: Transformer, dev$\_$examples: List$[$LetterCountingExample$],$ do$\_$print$=$False, do$\_$plot$\_$attn$=$False$)$:

$"""$

Decodes the given dataset, does plotting and printing of examples, and prints the final accuracy.

:param model: your Transformer that returns log probabilities at each position in the input

:param dev$\_$examples: the list of LetterCountingExample

:param do$\_$print: True if you want to print the input$/$gold$/$predictions for the examples, false otherwise

:param do$\_$plot$\_$attn: True if you want to write out plots for each example, false otherwise

:return:

$"""$

num$\_$correct $=0$

num$\_$total $=0$

if len$($dev$\_$examples$)>100$:

print$("$Decoding on a large number of examples $(\%$i$)$; not printing or plotting" $\%$ len$($dev$\_$examples$))$

do$\_$print $=$ False

do$\_$plot$\_$attn $=$ False

for i in range$($len$($dev$\_$examples$))$:

ex $=$ dev$\_$examples$[$i$]$

$($log$\_$probs, attn$\_$maps$)=$ model$($ex$.$input$\_$tensor.unsqueeze$(0))$ # Add batch dimension

predictions $=$ np$.$argmax$($log$\_$probs.detach$().$numpy$(),$ axis$=1)$

if do$\_$print:

print$("$INPUT $\%$i: $\%$s$"\%($i$,$ ex$.$input$))$

print$("$GOLD $\%$i: $\%$s$"\%($i$,$ repr$($ex$.$output.astype$($dtype$=$int$))))$

print$("$PRED $\%$i: $\%$s$"\%($i$,$ repr$($predictions$)))$

if do$\_$plot$\_$attn:

for j in range$($len$($attn$\_$maps$))$:

attn$\_$map $=$ attn$\_$maps$[$j$]$

fig, ax $=$ plt$.$subplots$()$

im $=$ ax$.$imshow$($attn$\_$map.detach$().$numpy$(),$ cmap$=$'hot', interpolation$=$'nearest'$)$

plt$.$colorbar$($im$)$

plt$.$title$("$Attention Map for Input $\%$i$,$ Head $\%$i$"\%($i$,$ j$))$

plt$.$savefig$($f$"$attention$\_$map$\_$input$\_\{$i$\}\_$head$\_\{$j$\}.$png$")$

plt$.$close$($fig$)$

num$\_$total $+=$ len$($ex$.$output$)$

num$\_$correct $+=($predictions $==$ ex$.$output$).$sum$()$

print$("$Accuracy: $\%\mathrm{.}2$f$\%\%"\%($num$\_$correct $*100\mathrm{.}0/$ num$\_$total$))$