parameters: (batch_size: int,
                           sequence_length: int,
                           num_global_tokens: int,
                           device: torch.device,
                           vocab_size: int = 100)
return_statement: return LongformerInputs(input_ids, attention_mask, global_attention_mask)
docstring: Create random inputs for Longformer model.
          Returns torch tensors of input_ids, attention_mask and global_attention_mask tensors.
docstring_summary: Create random inputs for Longformer model.
          Returns torch tensors of input_ids, attention_mask and global_attention_mask tensors.
docstring_tokens: Create random inputs for Longformer model . Returns torch tensors of input_ids attention_mask and global_attention_mask tensors .
function: def get_dummy_inputs(batch_size: int,
                           sequence_length: int,
                           num_global_tokens: int,
                           device: torch.device,
                           vocab_size: int = 100) -> LongformerInputs:
          """ Create random inputs for Longformer model.
          Returns torch tensors of input_ids, attention_mask and global_attention_mask tensors.
          """
  
          input_ids = torch.randint(low=0,
                                    high=vocab_size - 1,
                                    size=(batch_size, sequence_length),
                                    dtype=torch.long,
                                    device=device)
          attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=device)
          global_attention_mask = torch.zeros(input_ids.shape, dtype=torch.long, device=device)
          global_token_index = list(range(num_global_tokens))
          global_attention_mask[:, global_token_index] = 1
          return LongformerInputs(input_ids, attention_mask, global_attention_mask)
function_tokens: def get_dummy_inputs ( batch_size : int , sequence_length : int , num_global_tokens : int , device : torch . device , vocab_size : int = 100 ) -> LongformerInputs : input_ids = torch . randint ( low = 0 , high = vocab_size - 1 , size = ( batch_size , sequence_length ) , dtype = torch . long , device = device ) attention_mask = torch . ones ( input_ids . shape , dtype = torch . long , device = device ) global_attention_mask = torch . zeros ( input_ids . shape , dtype = torch . long , device = device ) global_token_index = list ( range ( num_global_tokens ) ) global_attention_mask [ : , global_token_index ] = 1 return LongformerInputs ( input_ids , attention_mask , global_attention_mask )