🐛 [Bug] Serialization of Graph Break Fails

Bug Description

Serialization of Graph Break Fails

To Reproduce

Steps to reproduce the behavior:

import torch
import torch.nn as nn
import torch_tensorrt as torchtrt
import torchvision

torch.manual_seed(0)
torch.cuda.manual_seed_all(0)

model = torchvision.models.resnet50().eval().to("cuda")
input = [torch.randn((1, 3, 224, 224)).to("cuda")]

BATCH = torch.export.Dim("BATCH", min=1, max=16)
exp_program = torch.export.export(model, tuple(input))#, dynamic_shapes=({0: BATCH}, ))


trt_mod1 = trt_gm = torchtrt.dynamo.compile(
        exp_program,
        tuple(input),
        # use_python_runtime=use_python_runtime,
        enabled_precisions={torch.float},
        min_block_size=1,
        immutable_weights=False,
        torch_executed_ops={torch.ops.aten.add.Tensor},
        reuse_cached_engines=False,
    )
serialized = torchtrt.save(trt_mod1, "/home/other/trt_mod1.pt2", inputs=input)
trt_mod_saved = torch.export.load("/home/other/trt_mod1.pt2").module()

Expected behavior

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0):
• PyTorch Version (e.g. 1.0):
• CPU Architecture:
• OS (e.g., Linux):
• How you installed PyTorch (conda, pip, libtorch, source):
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version:
• CUDA version:
• GPU models and configuration:
• Any other relevant information:

Additional context

Reproduce the issue with the provided ResNet50 example, focusing on torchtrt.dynamo.compile, torchtrt.save, and torch.export.load. Inspect the serialization path used by these entry points and document the failure with the relevant environment details. Done means the compiled module can be saved and loaded successfully for this reproduction.