code: def model_factory(C, D, X, N, drop_path, class_num, distillation):
      embed_dim = [int(x) for x in C.split("_")]
      num_heads = [int(x) for x in N.split("_")]
      depth = [int(x) for x in X.split("_")]
      act = nn.Hardswish
      model = LeViT(
          patch_size=16,
          embed_dim=embed_dim,
          num_heads=num_heads,
          key_dim=[D] * 3,
          depth=depth,
          attn_ratio=[2, 2, 2],
          mlp_ratio=[2, 2, 2],
          down_ops=[
              # ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
              ["Subsample", D, embed_dim[0] // D, 4, 2, 2],
              ["Subsample", D, embed_dim[1] // D, 4, 2, 2],
          ],
          attention_activation=act,
          mlp_activation=act,
          hybrid_backbone=b16(embed_dim[0], activation=act),
          class_num=class_num,
          drop_path=drop_path,
          distillation=distillation,
      )
  
      return model
  
imports: import itertools
  import math
  import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import TruncatedNormal
  from paddle.regularizer import L2Decay
  from .vision_transformer import Identity
  from .vision_transformer import ones_
  from .vision_transformer import trunc_normal_
  from .vision_transformer import zeros_
label_desc: Use image node_id 3 for calling a global function with example usage: model_factory(C, D, X, N, drop_path, class_num, distillation) and returns: model