class_docstr: Window based multi-head self-attention (W-MSA) module with relative
  position bias.
  
  Args:
      embed_dims (int): Number of input channels.
      num_heads (int): Number of attention heads.
      window_size (tuple[int]): The height and width of the window.
      qkv_bias (bool, optional):  If True, add a learnable bias to q, k, v.
          Default: True.
      qk_scale (float | None, optional): Override default qk scale of
          head_dim ** -0.5 if set. Default: None.
      attn_drop_rate (float, optional): Dropout ratio of attention weight.
          Default: 0.0
      proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
      init_cfg (dict | None, optional): The Config for initialization.
          Default: None.
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code: def double_step_seq(step1, len1, step2, len2):
      seq1 = torch.arange(0, step1 * len1, step1)
      seq2 = torch.arange(0, step2 * len2, step2)
      return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
  
imports: import warnings
  from collections import OrderedDict
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.utils.checkpoint
  from mmcv.cnn import build_norm_layer
  from mmcv.cnn.bricks.transformer import FFN, build_dropout
  from mmengine.logging import MMLogger
  from mmengine.model import BaseModule, ModuleList
  from mmengine.model.weight_init import constant_init, trunc_normal_, trunc_normal_init
  from mmengine.runner.checkpoint import CheckpointLoader
  from mmengine.utils import to_2tuple
  from mmdet.registry import MODELS
  from ..layers import PatchEmbed, PatchMerging
label_desc: Use image node_id 4 for calling the WindowMSA obj's underlying member method code with example usage: obj.double_step_seq(step1, len1, step2, len2) and returns: unknown