how do I fix this error regarding a distributed machine learning model? [closed]

I have written this code for this coding problem with the help from Claude:

The data used in this exercise are placed in the data folder. The train_x and train_y files contain training features and tags, respectively. Test_x and test_y files are for test data. You can read the data with the help of the numpy library. The data files of the features are saved as float16 to reduce the size, and in order to be able to train your model on them, they must be converted to float32 format. Create a model including feedforward, batchnorm, and buffer layers that can achieve a good accuracy (above 80%) on the test data (use the desired batch size and optimizer). Explain the architecture of the model.

from google.colab import drive

drive.mount('/content/drive')

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np

# Load data
train_x = torch.from_numpy(np.load('/content/drive/MyDrive/CHW4/train_x.npy').astype(np.float32))
train_y = torch.from_numpy(np.load('/content/drive/MyDrive/CHW4/train_y.npy'))
test_x = torch.from_numpy(np.load('/content/drive/MyDrive/CHW4/test_x.npy').astype(np.float32))
test_y = torch.from_numpy(np.load('/content/drive/MyDrive/CHW4/test_y.npy'))

num_classes = train_y.shape[0]

# Model architecture
class Model(nn.Module):
  def __init__(self):
    super().__init__()
    self.layer1 = nn.Linear(in_features=train_x.shape[1], out_features=64)
    self.bn1 = nn.BatchNorm1d(num_features=64)
    self.layer2 = nn.Linear(in_features=64, out_features=32)
    self.bn2 = nn.BatchNorm1d(num_features=32)
    self.out = nn.Linear(in_features=32, out_features = num_classes)
    
    def forward(self, x):
      x = torch.relu(self.bn1(self.layer1(x)))
      x = torch.relu(self.bn2(self.layer2(x)))
      x = self.out(x)
      return x

# Train function
def train(model, optimizer, train_x, train_y, test_x, test_y):
    criterion = nn.CrossEntropyLoss()
    model.train()
    for epoch in range(100):
        optimizer.zero_grad()
        output = model(train_x)
        loss = criterion(output, train_y)
        loss.backward()
        optimizer.step()

        if epoch%10 == 0:
            test_output = model(test_x)
            acc = (test_output.argmax(dim=1) == test_y).float().mean()
            print(f'Epoch {epoch} test accuracy: {acc:.2f}')

    print('Finished training')

num_classes = train_y.shape[0]
a = num_classes
model = Model(a)
optimizer = optim.Adam(model.parameters())
train(model, optimizer, train_x, train_y, test_x, test_y)

but I get this error:

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-31-228926b9ff0e> in <cell line: 54>()
     52 num_classes = train_y.shape[0]
     53 a = num_classes
---> 54 model = Model(a)
     55 optimizer = optim.Adam(model.parameters())
     56 train(model, optimizer, train_x, train_y, test_x, test_y)

TypeError: Model.__init__() takes 1 positional argument but 2 were given

How can I fix this?