code: def generate_dummy_inputs(
      self,
      tokenizer: "PreTrainedTokenizerBase",
      batch_size: int = -1,
      seq_length: int = -1,
      is_pair: bool = False,
      framework: Optional[TensorType] = None,
  ) -> Mapping[str, Any]:
      inputs = super().generate_dummy_inputs(
          preprocessor=tokenizer,
          batch_size=batch_size,
          seq_length=seq_length,
          is_pair=is_pair,
          framework=framework,
      )
      import torch
  
      inputs["global_attention_mask"] = torch.zeros_like(
          inputs["input_ids"]
      )
      # make every second token global
      inputs["global_attention_mask"][:, ::2] = 1
      return inputs
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/longformer/configuration_longformer.py
imports: from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import TensorType, logging
label_desc: Use image node_id 6 for calling the LongformerOnnxConfig obj's underlying member method code with example usage: obj.generate_dummy_inputs(tokenizer, batch_size, seq_length, is_pair, framework) and returns: inputs