attention model failed to convert, "error: unknown: unsupported by backend contract: module initializers"

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attention model can't convert to stablehlo IR.
Qustion:
How can I solve this problem.
python script:
```
import torch
import torch.nn as nn
import torch.nn.init as init
import torch_mlir
class AttentionModel(nn.Module):
def __init__(self, embed_size, num_heads):
super(AttentionModel, self).__init__()
self.attention = nn.MultiheadAttention(embed_size, num_heads)

def forward(self, query, key, value):
attn_output, attn_weights = self.attention(query, key, value)
return attn_output, attn_weights

embed_size = 64
num_heads = 8

model = AttentionModel(embed_size, num_heads)

model.eval()
seq_len = 10
batch_size = 1

query = torch.randn(seq_len, batch_size, embed_size)
key = torch.randn(seq_len, batch_size, embed_size)
value = torch.randn(seq_len, batch_size, embed_size)

module = torch_mlir.compile(model, (query, key, value), output_type=torch_mlir.OutputType.STABLEHLO)

with open("model.mlir", "w") as f:
f.write(str(module))
```

```
Traceback (most recent call last):
File "/home/x/work/kneron_mlir/torch_example/attention/attention.py", line 37, in
module = torch_mlir.compile(model, (query, key, value), output_type=torch_mlir.OutputType.STABLEHLO)
File "/home/x/work/kneron_mlir/torch-build/python_packages/torch_mlir/torch_mlir/__init__.py", line 451, in compile
run_pipeline_with_repro_report(
File "/home/x/work/kneron_mlir/torch-build/python_packages/torch_mlir/torch_mlir/compiler_utils.py", line 69, in run_pipeline_with_repro_report
raise TorchMlirCompilerError(trimmed_message) from None
torch_mlir.compiler_utils.TorchMlirCompilerError: Lowering TorchScript IR -> Torch Backend IR failed with the following diagnostics:

python exception: Failure while executing pass pipeline:
error: unknown: unsupported by backend contract: module initializers
note: unknown: see current operation: "torch.initialize.global_slots"(%6, %7, %8, %9) <{slotSymNames = [@attention.in_proj_weight, @attention.in_proj_bias, @attention.out_proj.weight, @attention.out_proj.bias]}> : (!torch.tensor<[192,64],f32>, !torch.tensor<[192],f32>, !torch.tensor<[64,64],f32>, !torch.tensor<[64],f32>) -> ()
note: unknown: this is likely due to InlineGlobalSlots being unable to inline a global slot
```
If use_tracing=True, It can convert model to stable IR.
`module = torch_mlir.compile(model, (query, key, value), output_type=torch_mlir.OutputType.STABLEHLO,use_tracing=True)`

Reproduce the failure from attention.py with the provided AttentionModel and StableHLO compilation call. Start with torch_mlir.compile in __init__.py and the InlineGlobalSlots diagnostic, comparing the regular path with use_tracing=True. Done means the model converts to StableHLO without the module-initializer error.

Written by the indexing model from the issue text.