class_docstr: Channel-wise self-attention block. Modified from DDPM.
class_docstr_tok: ["Channel-wise","self-attention","block",".","Modified","from","DDPM","."]
code: def forward(self, x):
      B, C, H, W = x.shape
      h = self.GroupNorm_0(x)
      q = self.NIN_0(h)
      k = self.NIN_1(h)
      v = self.NIN_2(h)
  
      w = torch.einsum("bchw,bcij->bhwij", q, k) * (int(C) ** (-0.5))
      w = torch.reshape(w, (B, H, W, H * W))
      w = F.softmax(w, dim=-1)
      w = torch.reshape(w, (B, H, W, H, W))
      h = torch.einsum("bhwij,bcij->bchw", w, v)
      h = self.NIN_3(h)
      if not self.skip_rescale:
          return x + h
      else:
          return (x + h) / np.sqrt(2.0)
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet2.enh.layers.ncsnpp_utils import layers, up_or_down_sampling
label_desc: Use image node_id 2 for calling the AttnBlockpp obj's underlying member method code with example usage: obj.forward(x) and returns: unknown, unknown