code: def get_attention_biases(self, device: torch.device) -> torch.Tensor:
      if torch.jit.is_tracing() or self.training:
          return self.attention_biases[:, self.attention_bias_idxs]
      else:
          device_key = str(device)
          if device_key not in self.attention_bias_cache:
              self.attention_bias_cache[
                  device_key
              ] = self.attention_biases[:, self.attention_bias_idxs]
          return self.attention_bias_cache[device_key]
  
imports: import itertools
  from collections import OrderedDict
  from typing import Dict
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SqueezeExcite, SelectAdaptivePool2d, trunc_normal_, _assert
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 3 for calling the CascadedGroupAttention obj's underlying member method code with example usage: obj.get_attention_biases(device) and returns: self, self