Fix onnx.Gather lowering with dynamic shapes (#3675)

Supports the result with dynamic shape and scalar indices like
```
func.func @test_gather_scalar(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32> attributes {torch.onnx_meta.opset_version = 13 : si64} {
  %0 = torch.operator "onnx.Gather"(%arg0, %arg1) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3,4,5],f32>, !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32>
  return %0 : !torch.vtensor<[?,?],f32>
}
```

`Torch::AtenSqueezeOp` is referring to the result shape, so it will
failed on lowering if the result shape is dynamic.

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -1941,7 +1941,6 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
             indicesCt = Torch::kUnknownSize;
             break;
           }
-
           indicesCt *= sz;
         }
 
@@ -1976,8 +1975,10 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
           return success();
         }
 
-        rewriter.replaceOpWithNewOp<Torch::AtenSqueezeOp>(binder.op, resultType,
-                                                          gather);
+        // indicesRank = 0 will select 1 from the axis dim and squeeze it
+        // Use AtenSqueezeDimOp for the case of result with dynamic shape
+        rewriter.replaceOpWithNewOp<Torch::AtenSqueezeDimOp>(
+            binder.op, resultType, gather, index);
         return success();
       });
   patterns.onOp(

test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir

@@ -78,7 +78,7 @@ func.func @test_gather_scalar(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.
   // CHECK: %[[SEL:.+]] = torch.aten.where.self %[[LT]], %[[ADD]], %arg1
   // CHECK: %[[FLAT:.+]] = torch.aten.unsqueeze %[[SEL]], %[[ZERO]] : !torch.vtensor<[],si64>, !torch.int -> !torch.vtensor<[1],si64>
   // CHECK: %[[ISEL:.+]] = torch.aten.index_select %arg0, %[[AXIS]], %[[FLAT]]
-  // CHECK: %[[RES:.+]] = torch.aten.squeeze %[[ISEL]] : !torch.vtensor<[1,4,5],f32> -> !torch.vtensor<[4,5],f32>
+  // CHECK: %[[RES:.+]] = torch.aten.squeeze.dim %[[ISEL]], %[[AXIS]] : !torch.vtensor<[1,4,5],f32>, !torch.int -> !torch.vtensor<[4,5],f32>
   // CHECK: return %[[RES]]
   %0 = torch.operator "onnx.Gather"(%arg0, %arg1) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3,4,5],f32>, !torch.vtensor<[], si64>) -> !torch.vtensor<[4,5],f32>
   return %0 : !torch.vtensor<[4,5],f32>