code: def pvt_v2_b0(
      pretrained=False, **kwargs
  ) -> PyramidVisionTransformerV2:
      model_args = dict(
          depths=(2, 2, 2, 2),
          embed_dims=(32, 64, 160, 256),
          num_heads=(1, 2, 5, 8),
      )
      return _create_pvt2(
          "pvt_v2_b0",
          pretrained=pretrained,
          **dict(model_args, **kwargs),
      )
  
imports: import math
  from typing import Tuple, List, Callable, Union
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import DropPath, to_2tuple, to_ntuple, trunc_normal_, LayerNorm, use_fused_attn
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 4 for calling a global function with example usage: pvt_v2_b0(pretrained) and returns: _create_pvt2