here is the code for an app, don't say anything because i will ask you after that, **code**: import torch
import torch.nn as nn
import torch.optim as optim
import json
import base64
from torch.utils.data import DataLoader, Dataset
import binascii
import chardet
import random
import math


class PositionalEncoding(nn.Module):
    def __init__(self, embedding_dim, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.embedding_dim = embedding_dim
        
        # Creating positional encoding matrix
        pe = torch.zeros(max_len, embedding_dim)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embedding_dim, 2).float() * (-math.log(10000.0) / embedding_dim))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        
        # Registering pe as a buffer since it’s not a model parameter
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x

class DecoderLayer(nn.Module):
    def __init__(self, embedding_dim, heads, ff_dim):
        super(DecoderLayer, self).__init__()
        self.self_attn = nn.MultiheadAttention(embedding_dim, heads)
        
        # Feedforward network
        self.ffn = nn.Sequential(
            nn.Linear(embedding_dim, ff_dim),
            nn.ReLU(),
            nn.Linear(ff_dim, embedding_dim),
        )
        
        self.layer_norm1 = nn.LayerNorm(embedding_dim)
        self.layer_norm2 = nn.LayerNorm(embedding_dim)
    
    def forward(self, src):
        src2 = self.layer_norm1(src)
        attn_output, _ = self.self_attn(src2, src2, src2)
        src = src + attn_output
        src2 = self.layer_norm2(src)
        src = src + self.ffn(src2)
        return src

class Decoder(nn.Module):
    def __init__(self, vocab_size, embedding_dim, num_layers, heads, ff_dim):
        super(Decoder, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.pos_encoding = PositionalEncoding(embedding_dim)
        self.decoder_layers = nn.ModuleList([DecoderLayer(embedding_dim, heads, ff_dim) for _ in range(num_layers)])
        self.final_layer = nn.Linear(embedding_dim, vocab_size)
    
    def forward(self, x):
        x = self.embedding(x)
        x = self.pos_encoding(x)
        
        for layer in self.decoder_layers:
            x = layer(x)
        
        output = self.final_layer(x)
        return output
  
class QAJsonlDataset(Dataset):
    def __init__(self, path, seq_len=512):
        super().__init__()
        self.pairs = self.load_data(path)
        self.seq_len = seq_len
        self.vocab = self.build_vocab([word for pair in self.pairs for item in pair for word in item])

    def __len__(self):
        return len(self.pairs)

    def __getitem__(self, idx):
        question, answer = self.pairs[idx]
        question_tokenized = self.tokenize([self.vocab[word] for word in question])
        answer_tokenized = self.tokenize([self.vocab[word] for word in answer])
        return torch.tensor(question_tokenized), torch.tensor(answer_tokenized)

    def load_data(self, path):
        pairs = []
        with open(path, "rb") as f:  # Open in binary mode
            for line in f:
                data = json.loads(line.decode("utf-8"))  # Decode binary data to JSON
                question, answer = self.decode_binary_data(data["user"], data["content"])  # Decode binary fields
                pairs.append((question.split(), answer.split()))
        return pairs

    def decode_binary_data(self, question_data, answer_data):
        try:
            question_encoding = chardet.detect(question_data.encode("utf-8"))['encoding']  # Detect encoding
            try:
                question = base64.b64decode(question_data).decode(question_encoding)
            except binascii.Error:
                try:
                    question = base64.b64decode(question_data.encode("utf-8") + b'=' * (-len(question_data) % 4)).decode(question_encoding)
                except binascii.Error:
                    print("Error decoding question (might be corrupt).")
                    question = ""

            answer_encoding = chardet.detect(answer_data.encode("utf-8"))['encoding']  # Detect encoding for answer
            try:
                answer = base64.b64decode(answer_data).decode(answer_encoding)
            except binascii.Error:
                try:
                    answer = base64.b64decode(answer_data.encode("utf-8") + b'=' * (-len(answer_data) % 4)).decode(answer_encoding)
                except binascii.Error:
                    print("Error decoding answer (might be corrupt).")
                    answer = ""

        except UnicodeDecodeError:
            print("Error decoding binary data (invalid encoding).")
            question = ""
            answer = ""

        return question, answer

    def tokenize(self, integer_sequence):
        tokens = integer_sequence[:self.seq_len]
        if len(tokens) < self.seq_len:
        # Pad with appropriate binary token
            padding_token = self.vocab["<pad>"]  # Consider a binary padding token if needed
            tokens.extend([padding_token] * (self.seq_len - len(tokens)))
        # Add <eos> token only if appropriate for binary representation
        return tokens

    def build_vocab(self, binary_tokens):
        # Your implementation here. This is a placeholder.
        unique_tokens = list(set(binary_tokens))
        vocab = {token: idx for idx, token in enumerate(unique_tokens)}
        vocab['<pad>'] = len(vocab)  # Adding a padding token
        return vocab

class DataLoader(Dataset):
    def __init__(self, dataset, batch_size=32):
        self.dataset = dataset
        self.batch_size = batch_size

    def __len__(self):
        return len(self.dataset) // self.batch_size

    def __getitem__(self, idx):
        batch = self.dataset[idx * self.batch_size:(idx + 1) * self.batch_size]
        inputs, targets = zip(*batch)  # Assuming inputs and targets are tensors
        inputs_padded = torch.nn.utils.rnn.pad_sequence(inputs, batch_first=True, padding_value=0)  # Using PyTorch's pad_sequence
        targets_padded = torch.nn.utils.rnn.pad_sequence(targets, batch_first=True, padding_value=0)
        return inputs_padded, targets_padded

# Define model parameters
vocab_size = 10000  # Adjust based on your vocabulary size
embedding_dim = 256
num_layers = 2  # Number of decoder layers
heads = 4  # Number of attention heads
ff_dim = 1024  # Feed-forward dimension

# Create decoder-only transformer model
model = Decoder(vocab_size, embedding_dim, num_layers, heads, ff_dim)

# Data loading and splitting
path_to_data = "data/Real_talk.jsonl"
dataset = QAJsonlDataset(path_to_data, 512)

# Shuffle the dataset
random.shuffle(dataset.pairs)

train_size = int(0.8 * len(dataset))  # 80% of data for training, adjust as necessary
val_size = len(dataset) - train_size
train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32)

# Define optimizer
optimizer = optim.Adam(model.parameters())
loss_fn = nn.CrossEntropyLoss()  # PyTorch version of SparseCategoricalCrossentropy with from_logits=True

# Training loop (example)
for epoch in range(1, 11):
    for i, (inputs, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(inputs)
        loss = loss_fn(output.view(-1, vocab_size), targets.view(-1))  # Reshape for CrossEntropyLoss
        loss.backward()
        optimizer.step()

    print(f'Epoch {epoch}, Loss: {loss.item()}')

def evaluate(model, val_loader):
  """
  Evaluates the model on the validation set and returns accuracy.
  """
  correct = 0
  total = 0
  with torch.no_grad():
    for inputs, targets in val_loader:
      outputs = model(inputs)
      predictions = torch.argmax(outputs, dim=1)
      correct += (predictions == targets).sum().item()
      total += targets.size(0)
  accuracy = correct / total
  return accuracy

# After training loop:
val_accuracy = evaluate(model, val_loader)
print(f'Validation Accuracy: {val_accuracy}')