IndexError: too many indices for tensor of dimension 2: When adding custom layer on HuggingFace model

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I've tried to add custom layers to HuggingFace Transformer model on binary classification task. As an absolute beginner, I tried to follow this tutorial

Here's the custom model

class CustomModel(nn.Module):
  def __init__(self,checkpoint,num_labels): 
    super(CustomModel,self).__init__() 
    self.num_labels = num_labels 

    #Load Model with given checkpoint and extract its body
    self.model =  AutoModelForSequenceClassification.from_pretrained(checkpoint,config=AutoConfig.from_pretrained(checkpoint, output_attentions=True,output_hidden_states=True))
    self.dropout = nn.Dropout(0.1) 
    self.classifier = nn.Linear(768,num_labels) # load and initialize weights

  def forward(self, input_ids=None, attention_mask=None,labels=None):
    #Extract outputs from the body
    outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)

    #Add custom layers
    sequence_output = self.dropout(outputs[0]) #outputs[0]=last hidden state

    logits = self.classifier(sequence_output[:,0,:].view(-1,768)) # calculate losses
    
    loss = None
    if labels is not None:
      loss_fct = nn.CrossEntropyLoss()
      loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
    
    return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states,attentions=outputs.attentions)

Unfortunately, it generated an error:

<ipython-input-32-5d5e07952b71> in forward(self, input_ids, attention_mask, labels)
     19     sequence_output = self.dropout(outputs[0]) #outputs[0]=last hidden state
     20 
---> 21     logits = self.classifier(sequence_output[:,0,:].view(-1,768)) # calculate losses
     22 
     23     loss = None

IndexError: too many indices for tensor of dimension 2

Also, you can find all of the code here.

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