Notes

Certainly! Here's the updated code for the `Trainer` class with the modifications I mentioned earlier:

```python
from typing import List
import random

class Trainer:
def __init__(self, model, tokenizer: Tokenizer, optimizer=None):
super().__init__()
self.model = model
if optimizer is None:
self.optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
else:
self.optimizer = optimizer
self.tokenizer = tokenizer
self.loss_function = torch.nn.CrossEntropyLoss()

def train(self, data: List[torch.Tensor], epochs, batch_size):
loss_per_epoch = []
for epoch in range(epochs):
losses = []

# Shuffle the sequences
random.shuffle(data)

# Create batches of sequences and their respective mask.
batches = []
for i in range(0, len(data), batch_size):
batch_data = data[i: i + batch_size]

# Create the input tensor for the batch
sequence_tensor = torch.nn.utils.rnn.pad_sequence(batch_data, batch_first=True).to(device)
mask_tensor = (sequence_tensor != self.tokenizer.character_to_token('<pad>')).to(device)

batches.append((sequence_tensor, mask_tensor))

# Train the model on each batch
for batch in batches:
self.model.train()

input_tensor, mask_tensor = batch

# Compute the model output
model_output, target = self.model.forward(x=input_tensor, mask=mask_tensor)

# Compute the losses
# The loss is computed on the model output and the target
loss = self.loss_function(model_output.transpose(1, 2), target)

# Backpropagate the loss.
loss.backward()

# Clip the gradients. This is used to prevent exploding gradients.
torch.nn.utils.clip_grad_norm_(self.model.parameters(), 0.5)

# Update the model parameters. This is done by taking a step in the direction of the gradient.
self.optimizer.step()

# Reset the gradients. This is done so that the gradients from the previous batch
# are not used in the next step.
self.optimizer.zero_grad()

# Append the loss to the list of losses, so that the average loss can be computed for this epoch.
losses.append(loss.item())

# Print the loss
epoch_loss = np.average(losses)
loss_per_epoch.append(epoch_loss)
print('Epoch:', epoch, 'Loss:', epoch_loss)

return loss_per_epoch
```

Please make sure to adjust the import statements and other relevant parts of your code as needed.