class_docstr: Interlaced sparse multi-head self attention module with relative position bias.
  Args:
      embed_dim (int): Number of input channels.
      num_heads (int): Number of attention heads.
      window_size (int, optional): Window size. Default: 7.
      rpe (bool, optional): Whether to use rpe. Default: True.
class_docstr_tok: ["Interlaced","sparse","multi-head","self","attention","module","with","relative","position","bias",".","Args",":","embed_dim","(","int",")",":","Number","of","input","channels",".","num_heads","(","int",")",":","Number","of","attention","heads",".","window_size","(","int",",","optional",")",":","Window","size",".","Default",":","7.","rpe","(","bool",",","optional",")",":","Whether","to","use","rpe",".","Default",":","True","."]
code: def forward(self, x, H, W, **kwargs):
      B, N, C = x.shape
      x = x.reshape([B, H, W, C])
      # pad
      x_pad = self.pad_helper.pad_if_needed(x, paddle.shape(x))
      # permute
      x_permute = self.permute_helper.permute(x_pad, x_pad.shape)
      # attention
      out, _, _ = self.attn(
          x_permute, x_permute, x_permute, rpe=self.with_rpe, **kwargs
      )
      # reverse permutation
      out = self.permute_helper.rev_permute(out, x_pad.shape)
      out = self.pad_helper.depad_if_needed(out, paddle.shape(x))
      return out.reshape([B, N, C])
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils, logger
  from paddleseg.models.backbones.transformer_utils import *
label_desc: Use image node_id 2 for calling the InterlacedPoolAttention obj's underlying member method code with example usage: obj.forward(x, H, W) and returns: out