Train this code and plot the loss function using tensorboard: import torch

import torch.nn as nn

import torch.optim as optim

from torch.utils.tensorboard import SummaryWriter

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

def $\_\_$init$\_\_($self$,$ input$\_$dim, hidden$\_$dim, output$\_$dim$)$:

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

self.lstm$1=$ nn$.$LSTM$($input$\_$dim, hidden$\_$dim, batch$\_$first$=$True$)$

self.linear$1=$ nn$.$Linear$($hidden$\_$dim, $32)$

self.lstm$2=$ nn$.$LSTM$(32,$ hidden$\_$dim, batch$\_$first$=$True$)$

self.linear$2=$ nn$.$Linear$($hidden$\_$dim, output$\_$dim$)$

self.softmax $=$ nn$.$Softmax$($dim$=1)$

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

out, $\_=$ self.lstm$1($x$)$

out $=$ torch.tanh$($out$)$

out $=$ self.linear$1($out$)$

out, $\_=$ self.lstm$2($out$)$

out $=$ self.linear$2($out$)$

out $=$ self.softmax$($out$)$

return out

# Example usage

input$\_$dim $=128$ # Example input dimension

hidden$\_$dim $=64$

output$\_$dim $=10$ # Number of classes

model $=$ NameDetector$($input$\_$dim, hidden$\_$dim, output$\_$dim$)$

# Sample data generation $($replace with actual data loader$)$

def generate$\_$dummy$\_$data$($num$\_$samples, input$\_$dim, seq$\_$len, num$\_$classes$)$:

X $=$ torch.randn$($num$\_$samples, seq$\_$len, input$\_$dim$)$

y $=$ torch.randint$(0,$ num$\_$classes, $($num$\_$samples,$))$

return X$,$ y

# Hyperparameters

num$\_$epochs $=20$

batch$\_$size $=32$

learning$\_$rate $=0\mathrm{.}001$

# Data

input$\_$dim $=128$

seq$\_$len $=10$

num$\_$classes $=10$

X$\_$train, y$\_$train $=$ generate$\_$dummy$\_$data$(1000,$ input$\_$dim, seq$\_$len, num$\_$classes$)$

train$\_$data $=$ torch.utils.data.TensorDataset$($X$\_$train, y$\_$train$)$

train$\_$loader $=$ torch.utils.data.DataLoader$($train$\_$data, batch$\_$size$=$batch$\_$size, shuffle$=$True$)$

# Model, loss function, and optimizer

model $=$ NameDetector$($input$\_$dim, hidden$\_$dim, num$\_$classes$)$

criterion $=$ nn$.$CrossEntropyLoss$()$

optimizer $=$ optim.Adam$($model$.$parameters$(),$ lr$=$learning$\_$rate$)$

# TensorBoard writer

writer $=$ SummaryWriter$()$

# Training loop

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

for i$,($inputs$,$ labels$)$ in enumerate$($train$\_$loader$)$:

outputs $=$ model$($inputs$)$

loss $=$ criterion$($outputs$,$ labels$)$

optimizer.zero$\_$grad$()$

loss.backward$()$

optimizer.step$()$

# Log loss to TensorBoard

writer.add$\_$scalar$(\text{'}$Loss$/$train$\text{'},$ loss.item$(),$ epoch $*$ len$($train$\_$loader$)+$ i$)$

print$($f$\text{'}$Epoch $[\{$epoch$+1\}/\{$num$\_$epochs$\}],$ Loss: $\{$loss$.$item$()$:$\mathrm{.}4$f$\}\text{'})$

writer.close$()$