[Python] IndexError: Target 1 is out of bounds__

I'm trying to write my first code in multi-class classification problem using Pytorch model and this error appeared in training loop. Could you tell me how can I fix this error

The Model:

import torch
from torch import nn

torch.manual_seed(1234)

class nn_model(nn.Module):
def __init__(self):
super().__init__()
self.layer1 = nn.Linear(len(X_train.columns), 128)
self.layer2 = nn.Linear(128, 64)
self.layer3 = nn.Linear(64, 1)
self.relu = nn.ReLU()
self.softmax = nn.Softmax()
def forward(self, x):
x = self.relu(self.layer1(x))
x = self.relu(self.layer2(x))
x = self.sigmoid(self.layer3(x))
return x

model = nn_model()
model.state_dict()


used loss function and optimizer:

optimizer = torch.optim.Adam(lr=0.001, params=model.parameters())

loss_function = torch.nn.CrossEntropyLoss()


The training and testing loop:

epochs = 10000

for epoch in range(1, epochs + 1):

# Training loop
model.train()
# 1. Do the forward pass

y_pred = model(torch.tensor(np.array(X_train)).float())

# 2. Calculate the loss

loss = loss_function(torch.round(y_pred).float(), torch.tensor(np.array(y_train)).long())

# 3. Optimizer Zero grad

optimizer.zero_grad()

# 4. Loss Backward

loss.backward()

# 5. Optimizer Step
optimizer.step()

if epoch % 1000 == 0:
model.eval()
with torch.inference_mode():

y_pred_val = model(torch.tensor(np.array(X_cross_val)))

loss_val = loss_function(torch.round(y_pred_val).long(), torch.tensor(np.array(y_cross_val)).long())


print(f'Epoch: {epoch} | Loss: {loss_val:4f}')

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