code: def _cfg(url: str = "", **kwargs):
      return {
          "url": url,
          "num_classes": 1000,
          "input_size": (3, 224, 224),
          "pool_size": (7, 7),
          "crop_pct": 0.875,
          "interpolation": "bilinear",
          "mean": IMAGENET_DEFAULT_MEAN,
          "std": IMAGENET_DEFAULT_STD,
          "first_conv": "conv_stem",
          "classifier": "classifier",
          **kwargs,
      }
  
imports: from functools import partial
  from typing import Callable, List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.checkpoint import checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import SelectAdaptivePool2d, Linear, LayerType, PadType, create_conv2d, get_norm_act_layer
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._efficientnet_blocks import SqueezeExcite
  from ._efficientnet_builder import BlockArgs, EfficientNetBuilder, decode_arch_def, efficientnet_init_weights, round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
  from ._features import FeatureInfo, FeatureHooks
  from ._manipulate import checkpoint_seq
  from ._registry import generate_default_cfgs, register_model, register_model_deprecations
label_desc: Use image node_id 7 for calling a global function with example usage: _cfg(url) and returns: dict