Render seems to not work about nnsmith HOT 6 CLOSED

from nnsmith.materialize import Render, BugReport, Model
from nnsmith.backends import BackendFactory

model_init = Model.init("torch", "cpu")
bug_report = BugReport.load(model_init, "./bug_example/", allow_partial=True)

render = Render()
render.emit_model(bug_report.testcase.model)
render.emit_input(bug_report.testcase.model)
from nnsmith.backends import BackendFactory
render.emit_backend(BackendFactory.init("pt2"))
output = render.render()
with open("./output.py", "w+") as f:
    f.write(output)

import numpy as np
import torch
import pickle

# Model definition
class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.v5_0 = torch.nn.Parameter(torch.empty([1], dtype=torch.int16), requires_grad=False)
    def forward(self, *args):
        _args = args
        getitem = _args[0];  _args = None
        _tensor_constant0 = self._tensor_constant0
        mul = torch.mul(_tensor_constant0, getitem);  _tensor_constant0 = None
        expand = mul.expand(1)
        expand_1 = mul.expand(1, 1, 1, 1);  mul = None
        max_1 = torch.max(expand_1, getitem);  expand_1 = getitem = None
        return (expand, max_1)

m = M()


# Initialize weight
# None

# Initialize input
inp = [np.zeros([], dtype='int16')]

# Compile the model
opt = torch.compile(m, fullgraph=True, backend='inductor', mode=None)

# Eager run
m_out = m(*[torch.from_numpy(v).to('cpu') for v in inp])
m_out = [v.cpu().detach() for v in m_out] # torch2numpy
m_out = [v.resolve_conj().numpy() if v.is_conj() else v.numpy() for v in m_out] # torch2numpy

# Compiled run
opt_out = opt(*[torch.from_numpy(v).to('cpu') for v in inp])
opt_out = [v.cpu().detach() for v in opt_out] # torch2numpy
opt_out = [v.resolve_conj().numpy() if v.is_conj() else v.numpy() for v in opt_out] # torch2numpy

# Differential testing
for i, (l, r) in enumerate(zip(m_out, opt_out)):
    np.testing.assert_allclose(l, r, rtol=1e-2, atol=1e-3, err_msg=f"Result mismatch @ index {i}")