code: def load_from_pretrained(self):
      model_urls = {
          0: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth",
          1: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b1-f1951068.pth",
          2: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b2-8bb594d6.pth",
          3: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b3-5fb5a3c3.pth",
          4: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b4-6ed6700e.pth",
          5: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b5-b6417697.pth",
          6: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b6-c76e70fd.pth",
          7: "https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b7-dcc49843.pth",
      }
  
      b0 = torch_load(fetch(model_urls[self.number]))
      for k, v in b0.items():
          if k.endswith("num_batches_tracked"):
              continue
          for cat in [
              "_conv_head",
              "_conv_stem",
              "_depthwise_conv",
              "_expand_conv",
              "_fc",
              "_project_conv",
              "_se_reduce",
              "_se_expand",
          ]:
              if cat in k:
                  k = k.replace(".bias", "_bias")
                  k = k.replace(".weight", "")
  
          # print(k, v.shape)
          mv = get_child(self, k)
          vnp = v  # .astype(np.float32)
          vnp = vnp if k != "_fc" else vnp.cpu().T
          # vnp = vnp if vnp.shape != () else np.array([vnp])
  
          if mv.shape == vnp.shape:
              mv.assign(vnp.to(mv.device))
          else:
              print(
                  "MISMATCH SHAPE IN %s, %r %r"
                  % (k, mv.shape, vnp.shape)
              )
  
file_path: tinygrad/extra/models/efficientnet.py
imports: import math
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d
  from tinygrad.helpers import get_child, fetch
  from tinygrad.nn.state import torch_load
label_desc: Use image node_id 3 for calling the EfficientNet obj's underlying member method code with example usage: obj.load_from_pretrained() without return types