Errors while tracing dpt_beit_large_384.pt

[3togo]

I got the following errors when tracing "dpt_beit_large_384.pt".

Any help?

Traceback (most recent call last):
  File "/work/gitee/MiDaS-cpp/python/export_model.py", line 162, in <module>
    convert(in_model_type, in_model_path, out_model_path)
  File "/work/gitee/MiDaS-cpp/python/export_model.py", line 84, in convert
    sm = torch.jit.trace(model, sample, strict=False)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/eli/.local/lib/python3.11/site-packages/torch/jit/_trace.py", line 794, in trace
    return trace_module(
           ^^^^^^^^^^^^^
  File "/home/eli/.local/lib/python3.11/site-packages/torch/jit/_trace.py", line 1084, in trace_module
    _check_trace(
  File "/home/eli/.local/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/eli/.local/lib/python3.11/site-packages/torch/jit/_trace.py", line 562, in _check_trace
    raise TracingCheckError(*diag_info)
torch.jit._trace.TracingCheckError: Tracing failed sanity checks!
ERROR: Graphs differed across invocations!
	Graph diff:
		  graph(%self.1 : __torch__.midas.dpt_depth.DPTDepthModel,
		        %x.1 : Tensor):
		    %scratch : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %output_conv : __torch__.torch.nn.modules.container.Sequential = prim::GetAttr[name="output_conv"](%scratch)
		    %scratch.15 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %refinenet1 : __torch__.midas.blocks.FeatureFusionBlock_custom = prim::GetAttr[name="refinenet1"](%scratch.15)
		    %scratch.13 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %refinenet2 : __torch__.midas.blocks.FeatureFusionBlock_custom = prim::GetAttr[name="refinenet2"](%scratch.13)
		    %scratch.11 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %refinenet3 : __torch__.midas.blocks.FeatureFusionBlock_custom = prim::GetAttr[name="refinenet3"](%scratch.11)
		    %scratch.9 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %refinenet4 : __torch__.midas.blocks.FeatureFusionBlock_custom = prim::GetAttr[name="refinenet4"](%scratch.9)
		    %scratch.7 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %layer4_rn : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="layer4_rn"](%scratch.7)
		    %scratch.5 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %layer3_rn : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="layer3_rn"](%scratch.5)
		    %scratch.3 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %layer2_rn : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="layer2_rn"](%scratch.3)
		    %scratch.1 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="scratch"](%self.1)
		    %layer1_rn : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="layer1_rn"](%scratch.1)
		    %pretrained : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="pretrained"](%self.1)
		    %act_postprocess4 : __torch__.torch.nn.modules.container.Sequential = prim::GetAttr[name="act_postprocess4"](%pretrained)
		    %_4.7 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="4"](%act_postprocess4)
		    %pretrained.83 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="pretrained"](%self.1)
		    %act_postprocess4.5 : __torch__.torch.nn.modules.container.Sequential = prim::GetAttr[name="act_postprocess4"](%pretrained.83)
		    %_3.9 : __torch__.torch.nn.modules.conv.Conv2d = prim::GetAttr[name="3"](%act_postprocess4.5)
		    %pretrained.81 : __torch__.torch.nn.modules.module.Module = prim::GetAttr[name="pretrained"](%self.1)
		    %act_postprocess3 : __torch__.torch.nn.modules.container.Sequential = prim::GetAttr[name="act_postprocess3"](%pretrained.81)