code: def _get_mask(to_dtype: torch.dtype, dim0: int, dim1: int):
      if to_dtype == torch.float:
          mask = torch.randint(0, 2, (dim0, dim1)).to(dtype=torch.bool)
          return mask.to(dtype=to_dtype).masked_fill(
              mask, -float("inf")
          )
      return torch.randint(0, 2, (dim0, dim1)).to(dtype=to_dtype)
  
file_path: fairseq/fairseq/benchmark/benchmark_multihead_attention.py
imports: import itertools
  import random
  import torch
  from torch.utils import benchmark
  from fairseq.modules.multihead_attention import MultiheadAttention
label_desc: Use image node_id 2 for calling a global function with example usage: _get_mask(to_dtype, dim0, dim1) and returns: torch, mask