🐛 [Bug] F.interpolate not support dynamic shape

Bug Description

To Reproduce

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_tensorrt


class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 3, kernel_size=3, stride=2, padding=1)

    def forward(self, x):
        x1 = self.conv(x)
        x1 = F.interpolate(x1, size=(x.shape[2], x.shape[3]), mode="nearest")
        x = x1 + x
        return x


if __name__ == '__main__':
    with torch.no_grad():
        device = torch.device("cuda")
        net = Net().to(device)
        net = net.eval()
        dummy_input = torch.randn(1, 3, 64, 64).to(device)

        with torch.jit.optimized_execution(False):
            traced_net = torch.jit.trace(net, dummy_input)
        
        trt_net = torch_tensorrt.compile(
            traced_net, 
            inputs = [
                torch_tensorrt.Input(min_shape=[1, 3, 16, 16], opt_shape=[4, 3, 64, 64], max_shape=[8, 3, 128, 128]),
            ],
            enabled_precisions = {torch.half},
            truncate_long_and_double = True,
            allow_shape_tensors = True,
            workspace_size = 1 << 30 # 1GB
        )

        print(net(dummy_input).shape)
        print(trt_net(dummy_input).shape)

log:

WARNING:torch_tensorrt._compile:Input graph is a Torchscript module but the ir provided is default (dynamo). Please set ir=torchscript to suppress the warning. Compiling the module with ir=torchscript
Traceback (most recent call last):
  File "xxx.py", line 29, in <module>
    trt_net = torch_tensorrt.compile(
  File "/home/xxx/miniconda3/lib/python3.8/site-packages/torch_tensorrt/_compile.py", line 185, in compile
    compiled_ts_module: torch.jit.ScriptModule = torchscript_compile(
  File "/home/xxx/miniconda3/lib/python3.8/site-packages/torch_tensorrt/ts/_compiler.py", line 151, in compile
    compiled_cpp_mod = _C.compile_graph(module._c, _parse_compile_spec(spec))
RuntimeError: [Error thrown at ./core/conversion/var/Var_inl.h:62] Expected ivalue->isIntList() to be true but got false
Requested unwrapping of arg IValue assuming it was N3c104ListIlEE however type is Any[]

Expected behavior

support dynamic size as input

Environment

Collecting environment information...
PyTorch version: 2.2.0.dev20230919+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.27.5
Libc version: glibc-2.31

Python version: 3.8.16 (default, Jan 17 2023, 23:13:24)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-83-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.86.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                              x86_64
CPU 运行模式：                      32-bit, 64-bit
字节序：                            Little Endian
Address sizes:                      43 bits physical, 48 bits virtual
CPU:                                48
在线 CPU 列表：                     0-47
每个核的线程数：                    2
每个座的核数：                      24
座：                                1
NUMA 节点：                         1
厂商 ID：                           AuthenticAMD
CPU 系列：                          23
型号：                              49
型号名称：                          AMD Ryzen Threadripper 3960X 24-Core Processor
步进：                              0
Frequency boost:                    enabled
CPU MHz：                           2200.000
CPU 最大 MHz：                      3800.0000
CPU 最小 MHz：                      2200.0000
BogoMIPS：                          7585.95
虚拟化：                            AMD-V
L1d 缓存：                          768 KiB
L1i 缓存：                          768 KiB
L2 缓存：                           12 MiB
L3 缓存：                           128 MiB
NUMA 节点0 CPU：                    0-47
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
标记：                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es

Versions of relevant libraries:
[pip3] msgpack-numpy==0.4.8
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.23.5
[pip3] pytorch-lightning==1.8.6
[pip3] pytorch-triton==2.1.0+6e4932cda8
[pip3] torch==2.2.0.dev20230919+cu121
[pip3] torch-tensorrt==2.2.0.dev20230919+cu121
[pip3] torchaudio==2.2.0.dev20230919+cu121
[pip3] torchmetrics==1.1.2
[pip3] torchvision==0.17.0.dev20230919+cu121
[pip3] triton==2.0.0
[conda] msgpack-numpy             0.4.8                    pypi_0    pypi
[conda] numpy                     1.23.5                   pypi_0    pypi
[conda] pytorch-lightning         1.8.6                    pypi_0    pypi
[conda] pytorch-triton            2.1.0+6e4932cda8          pypi_0    pypi
[conda] torch                     2.2.0.dev20230919+cu121          pypi_0    pypi
[conda] torch-tensorrt            2.2.0.dev20230919+cu121          pypi_0    pypi
[conda] torchaudio                2.2.0.dev20230919+cu121          pypi_0    pypi
[conda] torchmetrics              1.1.2                    pypi_0    pypi
[conda] torchvision               0.17.0.dev20230919+cu121          pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi