"RuntimeError: PyTorch convert function for op 'pythonop' not implemented" AND "Python builtin is currently not supported in Torchscript"

Question

Newbie question. I've been trying to convert this PyTorch model into CoreML model. I've followed the guide here but couldn't make it work. I tried tracing and scripting but faced errors which hint that there might be an operation not supported in TorchScript:

Error on torch.jit.trace: RuntimeError: PyTorch convert function for op 'pythonop' not implemented

Error on torch.jit.script: RuntimeError: Python builtin <built-in method apply of FunctionMeta object at 0x7fa37e2ad600> is currently not supported in Torchscript

I suspect that it just might not be possible to convert any PyTorch model into CoreML one. Is this the case? Can I somehow overcome the errors without diving deep into PyTorch operations and layers?

My python script just in case (model is loaded locally):

import warnings
import torch
import torch.nn as nn
import coremltools as ct

from efficientnet_pytorch import EfficientNet
from torchvision import datasets, models, transforms
from PIL import Image

# Simple loading the model
# model = torch.load('food308_efnetb2_91.31.pth', map_location=torch.device('cpu')) 
# ends up with RuntimeError("Could not get name of python class object")

# Load the model
model = EfficientNet.from_pretrained('efficientnet-b2')
num_ftrs = model._fc.in_features
model._fc = nn.Linear(num_ftrs, 308)
prev_state = torch.load('food308_efnetb2_91.31.pth', map_location=torch.device('cpu'))
model.load_state_dict(prev_state)
model.eval()

# Model tracing
example_input = torch.rand(1, 3, 224, 224)
traced_model = torch.jit.trace(model, example_input)
mlmodel = ct.convert(
    traced_model,
    inputs=[ct.TensorType(name="input", shape=(1, 3, 64, 64))],
)

# Model scripting
scripted_model = torch.jit.script(model)
mlmodel2 = ct.convert(
    scripted_model,
    inputs=[ct.TensorType(name="input", shape=(1, 3, 64, 64))],
)