code: def get_model(version, load_weights=False):
      model = ConvNeXt(**versions[version])
      if load_weights:
          from tinygrad.nn.state import torch_load
  
          weights = torch_load(
              fetch(
                  f"https://dl.fbaipublicfiles.com/convnext/convnext_{version}_1k_224_ema.pth"
              )
          )["model"]
          for k, v in weights.items():
              mv = get_child(model, k)
              mv.assign(v.reshape(mv.shape).to(mv.device)).realize()
      return model
  
file_path: tinygrad/extra/models/convnext.py
imports: from tinygrad.tensor import Tensor
  from tinygrad.nn import Conv2d, LayerNorm, LayerNorm2d, Linear
  from tinygrad.helpers import fetch, get_child
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(version, load_weights) and returns: model