ValueError: too many values to unpack (expected 2) or not enough values to unpack (expected 2, got 1)

I am trying to train T5 model binary classification. When I am trying todefine data class, I am getting

batch_size, seq_length = input_shape1017 # required mask seq length can be calculated via length of past1018 mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length

If I am using unsqueeze(0) for input_id and attention_mask I am getting following error

ValueError: too many values to unpack (expected 2)

if I am using squeeze(0), flatten() or none of them I am getting

not enough values to unpack (expected 2, got 1)

The data class is below......

  def __init__(self,               df: pd.DataFrame,               tokenizer:T5Tokenizer,               source_max_token_length: int=1000,               target_max_token_length: int=400):    self.tokenizer=tokenizer    self.df=df    self.source_max_token_length=source_max_token_length    self.target_max_token_length=target_max_token_length  def __len__(self):    return len(self.df)  def __getitem__(self,index: int):    data_row=self.df.iloc[index]    source_encoding=tokenizer.batch_encode_plus(      str(data_row['question']),      # str(data_row['context']),      max_length=self.source_max_token_length,      padding='max_length',      truncation='only_second',      return_attention_mask=True,      add_special_tokens=True,      return_tensors='pt'    )    target_encoding=tokenizer.batch_encode_plus(      str(data_row['answers']),      max_length=self.target_max_token_length,      padding='max_length',      truncation=True,      return_attention_mask=True,      add_special_tokens=True,      return_tensors='pt'    )    labels=target_encoding['input_ids']    labels[labels==0]=-100    return dict(        question=data_row['question'],        # context=data_row['context'],        answer_text=data_row['answers'],        # input_ids=np.array([source_encoding['input_ids']]),#.flatten(),np.array([d['input_ids']])        # attention_mask=np.array([source_encoding['attention_mask']]),#.flatten(),#.flatten(),        # labels=labels        input_ids=source_encoding['input_ids'].unsqueeze(0),#.flatten(),#.flatten(),np.array([d['input_ids']])        attention_mask=source_encoding['attention_mask'].unsqueeze(0),#.flatten(),#.flatten(),#.flatten(),        labels=labels.flatten()    )

I tried ever debugging like:

      str(data_row['question']),      # str(data_row['context']),      max_length=1000,      padding='max_length',      truncation='only_second',      return_attention_mask=True,      add_special_tokens=True,return_tensors='pt'      )

The size of example is example['input_ids'].shape

example['input_ids'].shapetorch.Size([61, 1000])

I think I do not need unsqueeze(0) or flatten(), but none of them works

sagemaker: 2.221.0transformers: 4.41.0pytorch_lightning: 1.1.3