Pytorch Beginner: TypeError in loss function

I'm a beginner in pytorch. I ran into this RuntimeError and I'm struggling to resolve it.
It says that the "result type" of the loss function is Float and cannot be cast to Long.
I have tried casting from float32 to LongTensor before the loss function runs, which resulted in another error inside the model("RuntimeError: mat1 and mat2 must have the same dtype").
I do not know how to resolve this Problem.

This is my code:

# Setup loss function
loss_fn = nn.BCEWithLogitsLoss()

# Random Seed
torch.manual_seed = RANDOM_SEED
torch.cuda.manual_seed = RANDOM_SEED

# Epochs
epochs = 1000

# Send data to device
X_train, X_test = X_train.to(device), X_test.to(device)
y_train, y_test = y_train.to(device), y_test.to(device)
# X_train, X_test = X_train.long(), X_test.long()
# y_train, y_test = y_train.long(), y_test.long()


# Training and testing loop
for epoch in range(epochs):
  model_0.train()
  
  # 1. Forward pass (logits -> prob -> label)
  y_logits = model_0(X_train).squeeze()
  y_pred = torch.round(torch.sigmoid(y_logits))

  # 2. Calculate loss and accuracy
  loss = loss_fn(y_logits,
                 y_train)
  acc = acc_fn(y_pred,
               y_train.int())

  # 3. Zero gradients
  optimizer.zero_grad()

  # 4. Loss backward (perform backpropagation)
  loss.backward()

  # 5. Optimizer step in gradient descent
  optimizer.step() 

  ### Testing
  model_0.eval()
  with torch.inference_mode():
    # 1. Forward pass
    test_logits = model_0(X_test).squeeze()
    test_pred = torch.round(torch.sigmoid(test_logits))

    # 2. Calculate test loss
    test_loss = loss_fn(test_logits,
                        y_test)
    test_acc = acc_fn(test_pred,
                      y_test)
    
    if epoch % 100 == 0:
      ep = str(epoch).zfill(4)
      print(f"Epoch: {ep} | Loss: {loss:.5f}, Acc: {acc:.2f}% | Test loss: {test_loss:.5f}, Test acc: {test_acc:.2f}%")

This is the error message I get:

RuntimeError                              Traceback (most recent call last)
<ipython-input-19-e09e5cb6e9d7> in <cell line: 13>()
     19 
     20   # 2. Calculate loss and accuracy
---> 21   loss = loss_fn(y_logits,
     22                  y_train)
     23   acc = acc_fn(y_pred,

2 frames
/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
   3163         raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
   3164 
-> 3165     return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
   3166 
   3167 

RuntimeError: result type Float can't be cast to the desired output type Long

Runtime Error Screenshot

Yes, this is very confusing. Actually what it wants are both in floats. You can pass then to float just using samething like this: y_train.float()