Review this code: import sys
import torch
import torch.nn as nn
from PyQt5 import QtWidgets, QtGui, QtCore
from transformers import BertTokenizer, GPT2Tokenizer, XLNetTokenizer

# Define the neural network model.
class RNNModel(nn.Module):
    def __init__(self, input_size, hidden_sizes, layer_types):
        super(RNNModel, self).__init__()
        self.layers = nn.ModuleList()
        for i, layer_type in enumerate(layer_types):
            input_dim = input_size if i == 0 else hidden_sizes[i - 1]
            if layer_type == 'LSTM':
                self.layers.append(nn.LSTM(input_dim, hidden_sizes[i], batch_first=True))
            elif layer_type == 'GRU':
                self.layers.append(nn.GRU(input_dim, hidden_sizes[i], batch_first=True))
            elif layer_type == 'Transformer':
                transformer_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=1)
                self.layers.append(nn.TransformerEncoder(transformer_layer, num_layers=1))
            else:
                raise ValueError(f"Unrecognized layer type: {layer_type}")

    def forward(self, x):
        for layer in self.layers:
            if isinstance(layer, nn.TransformerEncoder):
                x = layer(x)
            else:
                x, _ = layer(x)
        return x

# Define the custom GUI widget for configuring the language model.
class LanguageModelConfigurator(QtWidgets.QWidget):
    def __init__(self):
        super().__init__()
        self.initUI()

    def initUI(self):
        self.setGeometry(300, 300, 300, 200)
        self.setWindowTitle('Language Model Configuration')
        
        vbox = QtWidgets.QVBoxLayout(self)
        
        # Layer configuration section
        layer_config_group = QtWidgets.QGroupBox("Layer Configuration")
        layer_config_layout = QtWidgets.QVBoxLayout(layer_config_group)
        
        self.layer_list_widget = QtWidgets.QListWidget()
        layer_config_layout.addWidget(self.layer_list_widget)
        
        hbox_layers = QtWidgets.QHBoxLayout()
        self.layer_type_combo = QtWidgets.QComboBox()
        self.layer_type_combo.addItems(['LSTM', 'GRU', 'Transformer'])
        self.layer_size_spinbox = QtWidgets.QSpinBox()
        self.layer_size_spinbox.setMinimum(1)
        self.layer_size_spinbox.setMaximum(1024)
        
        hbox_layers.addWidget(self.layer_type_combo)
        hbox_layers.addWidget(self.layer_size_spinbox)
        
        add_layer_button = QtWidgets.QPushButton('Add Layer')
        add_layer_button.clicked.connect(self.add_layer)
        hbox_layers.addWidget(add_layer_button)
        
        layer_config_layout.addLayout(hbox_layers)
        vbox.addWidget(layer_config_group)
        
        # Tokenizer configuration section
        tokenizer_config_group = QtWidgets.QGroupBox("Tokenizer Configuration")
        tokenizer_config_layout = QtWidgets.QVBoxLayout(tokenizer_config_group)
        
        tokenizer_label = QtWidgets.QLabel('Choose a tokenizer:', self)

        tokenizer_config_layout.addWidget(tokenizer_label)
        
        self.tokenizer_combo = QtWidgets.QComboBox()
        self.tokenizer_combo.addItems([
            'bert-base-cased',
            'gpt2-medium',
            'xlnet-base-cased'
        ])
        tokenizer_config_layout.addWidget(self.tokenizer_combo)
        
        vbox.addWidget(tokenizer_config_group)
        
        # Submit button
        submit_button = QtWidgets.QPushButton('Generate Model', self)
        submit_button.clicked.connect(self.generate_model)
        vbox.addWidget(submit_button)
        
        self.setLayout(vbox)
        self.setStyleSheet("""
            QWidget {
                font-size: 14px;
            }
            QPushButton {
                background-color: #007BFF;
                border-style: none;
                padding: 6px 12px;
                color: white;
                border-radius: 4px;
            }
            QPushButton:hover {
                background-color: #0056b3;
            }
            QLabel {
                padding-bottom: 4px;
            }
        """)

    def add_layer(self):
        layer_type = self.layer_type_combo.currentText()
        layer_size = self.layer_size_spinbox.value()
        self.layer_list_widget.addItem(f'{layer_type} - Size: {layer_size}')

    def generate_model(self):
        layers = [
            (item.text().split(' - Size: ')[0], int(item.text().split(' - Size: ')[1]))
            for item in self.layer_list_widget.findItems('*', QtCore.Qt.MatchWildcard)
        ]
        layer_types, hidden_sizes = zip(*layers) if layers else ([], [])

        tokenizer_name = self.tokenizer_combo.currentText()
        tokenizer_class = {
            'bert-base-cased': BertTokenizer,
            'gpt2-medium': GPT2Tokenizer,
            'xlnet-base-cased': XLNetTokenizer
        }.get(tokenizer_name)
        if not tokenizer_class:
            print(f"Error: Unsupported tokenizer '{tokenizer_name}' selected.")
            return
        
        tokenizer = tokenizer_class.from_pretrained(tokenizer_name)
        input_size = tokenizer.model_max_length
        
        try:
            model = RNNModel(input_size, list(hidden_sizes), list(layer_types))
            print(model)
        except ValueError as e:
            print(str(e))

# Define the main window of the application.
class MainWindow(QtWidgets.QMainWindow):
    def __init__(self):
        super().__init__()
        self.initUI()

    def initUI(self):
        self.setWindowTitle('Model Training')
        self.language_model_configurator = LanguageModelConfigurator()
        self.setCentralWidget(self.language_model_configurator)
        self.show()

# Start the application.
def main():
    app = QtWidgets.QApplication(sys.argv)
    main_window = MainWindow()
    sys.exit(app.exec_())

if __name__ == "__main__":
    main()