code: def test_timm_sparse_attention(device):
      img_size = 224
      patch_size = 16
      batch = 8
  
      # Instantiate the reference model
      model = VisionTransformer(
          img_size=img_size,
          patch_size=patch_size,
          embed_dim=96,
          depth=8,
          num_heads=8,
          mlp_ratio=3.0,
          qkv_bias=False,
          norm_layer=torch.nn.LayerNorm,
      ).to(device)
  
      # Monkey patch all attentions to test the sparse-aware wrap
      def replace_attn_with_xformers_one(module, att_mask):
          module_output = module
          if isinstance(
              module, timm.models.vision_transformer.Attention
          ):
              qkv = module.qkv
              dim = qkv.weight.shape[1] * module.num_heads
              module_output = TimmSparseAttention(
                  dim, module.num_heads, attn_mask=att_mask
              )
          for name, child in module.named_children():
              module_output.add_module(
                  name, replace_attn_with_xformers_one(child, att_mask)
              )
          del module
  
          return module_output
  
      H, W = img_size // patch_size, img_size // patch_size
      mask = (
          torch.rand((H * W + 1, H * W + 1), device=device) > 0.5
      ).bool()
      model = replace_attn_with_xformers_one(model, att_mask=mask)
  
      # Check that we can throw a couple of random pictures at it
      inputs = torch.rand((batch, 3, img_size, img_size), device=device)
      _ = model(inputs)
  
imports: import pytest
  import torch
  import xformers
  from xformers.helpers.timm_sparse_attention import TimmSparseAttention
label_desc: Use image node_id 1 for calling a global function with example usage: test_timm_sparse_attention(device) and returns: module_output