code: def nest_base(pretrained=False, **kwargs) -> Nest:
      """Nest-B @ 224x224"""
      model_kwargs = dict(
          embed_dims=(128, 256, 512),
          num_heads=(4, 8, 16),
          depths=(2, 2, 20),
          **kwargs,
      )
      model = _create_nest(
          "nest_base", pretrained=pretrained, **model_kwargs
      )
      return model
  
imports: import collections.abc
  import logging
  import math
  from functools import partial
  import torch
  import torch.nn.functional
  from torch import nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import PatchEmbed, Mlp, DropPath, create_classifier, trunc_normal_, _assert
  from timm.layers import create_conv2d, create_pool2d, to_ntuple, use_fused_attn, LayerNorm
  from ._builder import build_model_with_cfg
  from ._features_fx import register_notrace_function
  from ._manipulate import checkpoint_seq, named_apply
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 8 for calling a global function with example usage: nest_base(pretrained) and returns: model