code: def convmixer_768_32(pretrained=False, **kwargs) -> ConvMixer:
      model_args = dict(
          dim=768,
          depth=32,
          kernel_size=7,
          patch_size=7,
          act_layer=nn.ReLU,
          **kwargs,
      )
      return _create_convmixer(
          "convmixer_768_32", pretrained, **model_args
      )
  
imports: import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectAdaptivePool2d
  from ._registry import register_model, generate_default_cfgs
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
label_desc: Use image node_id 4 for calling a global function with example usage: convmixer_768_32(pretrained) and returns: _create_convmixer