I followed the same code, provided in PyTorch [Tabular] — Binary Classification | by Akshaj Verma | Towards Data Science for my dataset.
here is the model
class binaryClassification(nn.Module):
def init(self):
super(binaryClassification, self).init()
# Number of input features is 25
self.layer_1 = nn.Linear(25, 64)
self.layer_2 = nn.Linear(64, 64)
self.layer_out = nn.Linear(64, 1)
    self.relu = nn.ReLU()
    self.dropout = nn.Dropout(p=0.1)
    self.batchnorm1 = nn.BatchNorm1d(64)
    self.batchnorm2 = nn.BatchNorm1d(64)
    
def forward(self, inputs):
    x = self.relu(self.layer_1(inputs))
    x = self.batchnorm1(x)
    x = self.relu(self.layer_2(x))
    x = self.batchnorm2(x)
    x = self.dropout(x)
    x = self.layer_out(x)
    
    return x

model.train()
for e in range(1, EPOCHS+1):
epoch_loss = 0
epoch_acc = 0
for X_batch, y_batch in train_loader:
X_batch, y_batch = X_batch.to(device), y_batch.to(device)
optimizer.zero_grad()
    y_pred = model(X_batch)
    
    loss = criterion(y_pred, y_batch.unsqueeze(1))
    acc = binary_acc(y_pred, y_batch.unsqueeze(1))
    
    loss.backward()
    optimizer.step()
    
    epoch_loss += loss.item()
    epoch_acc += acc.item()
    

print(f'Epoch {e+0:03}: | Loss: {epoch_loss/len(train_loader):.5f} | Acc: {epoch_acc/len(train_loader):.3f}')

at  the end it gives following error:
Target size [64,1,1] should be the same with inpur size  [64,1]
what is the issue?