[Torch Mlir] avg_pool1d function padding init value should be (0,)
Labels: triage review, oncall: jit

### 🐛 Describe the bug

  Currently in JITOperatorRegistryDump.txt the padding in  avg_pool1d  has the value List[int] = (0), this vill cause an error that ask you provide a default value of type  List[int] on parameter padding, which should be (0,) or something else


shape_function_signature = def aten〇avg_pool1d〡shape(self: List[int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0), ceil_mode: bool = False, count_include_pad: bool = True) -> List[int]:
  decomposition_function_signature = def aten〇avg_pool1d〡decomposition(self: Tensor, kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0), ceil_mode: bool = False, count_include_pad: bool = True) -> Tensor:
  dtype_function_signature = def aten〇avg_pool1d〡dtype(self_rank_dtype: Tuple[int, int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0), ceil_mode: bool = False, count_include_pad: bool = True) -> int:

### Error logs

RuntimeError:
Expected a default value of type List[int] on parameter "padding".:

def aten〇avg_pool1d〡shape(self: List[int], kernel_size: List[int], stride: List[int] = (), padding: List[int] = (0), ceil_mode: bool = False, count_include_pad: bool = True) -> List[int]:



### Minified repro

_No response_

### Versions

PyTorch version: 2.1.0.dev20230612+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 10 (buster) (x86_64)
GCC version: (GCC) 8.5.0
Clang version: 7.0.1-8+deb10u2 (tags/RELEASE_701/final)
CMake version: version 3.26.3
Libc version: glibc-2.28

Python version: 3.9.16 (main, Feb  4 2023, 02:19:02)  [GCC 8.5.0] (64-bit runtime)
Python platform: Linux-5.4.56.bsk.9-amd64-x86_64-with-glibc2.28
Is CUDA available: False
CUDA runtime version: 11.4.120
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB
GPU 1: Tesla V100-SXM2-32GB
GPU 2: Tesla V100-SXM2-32GB
GPU 3: Tesla V100-SXM2-32GB
GPU 4: Tesla V100-SXM2-32GB
GPU 5: Tesla V100-SXM2-32GB
GPU 6: Tesla V100-SXM2-32GB
GPU 7: Tesla V100-SXM2-32GB

Nvidia driver version: 470.129.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.4
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
Address sizes:       46 bits physical, 48 bits virtual
CPU(s):              96
On-line CPU(s) list: 0-95
Thread(s) per core:  2
Core(s) per socket:  24
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               85
Model name:          Intel(R) Xeon(R) Platinum 8260 CPU @ 2.40GHz
Stepping:            7
CPU MHz:             3100.011
CPU max MHz:         3900.0000
CPU min MHz:         1000.0000
BogoMIPS:            4800.00
Virtualization:      VT-x
L1d cache:           32K
L1i cache:           32K
L2 cache:            1024K
L3 cache:            36608K
NUMA node0 CPU(s):   0-23,48-71
NUMA node1 CPU(s):   24-47,72-95
Flags:               fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.1.0.dev20230612+cpu
[pip3] torchvision==0.16.0.dev20230523+cpu
[conda] Could not collect

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel @ezyang @msaroufim @wconstab @bdhirsh @anijain2305