[BUG] `Model.load` raises TypeError on Python 3.9+ (parameterized generic in isinstance)

[shaun0927]

Bug description

After PR #809 (commit 8bf122f5), transformers4rec.torch.model.base.Model.load() uses a parameterized generic as the second argument to isinstance:

Transformers4Rec/transformers4rec/torch/model/base.py

Line 917 in 8bf122f

if isinstance(state_dict, Dict[str, torch.Tensor]):

if isinstance(state_dict, Dict[str, torch.Tensor]):
    model.load_state_dict(state_dict, strict=strict)
else:
    raise ValueError("`state_dict` must be a dictionary of parameter (torch) tensors.")

On Python 3.9+ this raises TypeError: Subscripted generics cannot be used with class and instance checks before any of the load logic runs. Net effect: Model.load() is completely unusable on current main.

The regression is not caught by the existing test tests/unit/torch/model/test_model.py::test_save_next_item_prediction_model, which is the only place in the test suite that reaches this line. CI does not appear to exercise it on a clean env (likely the copy-pr-bot runners are currently blocked, see #798).

Steps/Code to reproduce bug

Pure-Python repro (no T4Rec install needed):

from typing import Dict
import torch

isinstance({"a": torch.zeros(1)}, Dict[str, torch.Tensor])
# TypeError: Subscripted generics cannot be used with class and instance checks

Library-level repro:

import torch
from transformers4rec.torch.model.base import Model

Model.load({"a": torch.zeros(1)}, heads=[])   # raises TypeError before anything else

Expected behavior

Model.load() should accept a plain dict (as returned by state_dict() or torch.load) and load the weights into the provided heads.

Environment details

• Transformers4Rec: main @ 8bf122f5
• Python: any 3.9+ (documented semantics; all modern installs affected)
• PyTorch: any

Additional context

Minimal fix — replace the parameterized generic with a plain dict:

if isinstance(state_dict, dict):
    model.load_state_dict(state_dict, strict=strict)
else:
    raise TypeError("`state_dict` must be a dict of torch.Tensor.")

The tighter "dict of str → torch.Tensor" check cannot be done in a single isinstance call; if such validation is desired it has to be a loop over items. In practice load_state_dict itself will surface incompatible entries, so the bare dict check is sufficient.

Happy to send a one-line PR.