parameters: (self, sents: List[List[str]], device: torch.device)
return_statement: return torch.t(sents_var)
docstring: Convert list of sentences (words) into tensor with necessary padding for 
          shorter sentences.
  
          @param sents (List[List[str]]): list of sentences (words)
          @param device: device on which to load the tesnor, i.e. CPU or GPU
  
          @returns sents_var: tensor of (max_sentence_length, batch_size)
docstring_summary: Convert list of sentences (words) into tensor with necessary padding for 
          shorter sentences.
docstring_tokens: Convert list of sentences ( words ) into tensor with necessary padding for shorter sentences .
function: def to_input_tensor(self, sents: List[List[str]], device: torch.device) -> torch.Tensor:
          """ Convert list of sentences (words) into tensor with necessary padding for 
          shorter sentences.
  
          @param sents (List[List[str]]): list of sentences (words)
          @param device: device on which to load the tesnor, i.e. CPU or GPU
  
          @returns sents_var: tensor of (max_sentence_length, batch_size)
          """
          word_ids = self.words2indices(sents)
          # print("0"*80)
          sents_t = pad_sents(word_ids, self['<pad>'])
          # print(sents_t)
          sents_var = torch.tensor(sents_t, dtype=torch.long, device=device)
          return torch.t(sents_var)
function_tokens: def to_input_tensor ( self , sents : List [ List [ str ] ] , device : torch . device ) -> torch . Tensor : word_ids = self . words2indices ( sents ) # print("0"*80) sents_t = pad_sents ( word_ids , self [ '<pad>' ] ) # print(sents_t) sents_var = torch . tensor ( sents_t , dtype = torch . long , device = device ) return torch . t ( sents_var )