class_docstr: Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
class_docstr_tok: ["Adapted","from","CLIP",":","https",":","\/\/github.com\/openai\/CLIP\/blob\/main\/clip\/model.py"]
code: def forward(self, x):
      b, c, *_spatial = x.shape
      x = x.reshape(b, c, -1)  # NC(HW)
      x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
      x = x + self.positional_embedding[None, :, :].to(
          x.dtype
      )  # NC(HW+1)
      x = self.qkv_proj(x)
      x = self.attention(x)
      x = self.c_proj(x)
      return x[:, :, 0]
  
imports: from abc import abstractmethod
  import math
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from ldm.modules.diffusionmodules.util import checkpoint, conv_nd, linear, avg_pool_nd, zero_module, normalization, timestep_embedding
  from ldm.modules.attention import SpatialTransformer
  from ldm.util import exists
label_desc: Use image node_id 2 for calling the AttentionPool2d obj's underlying member method code with example usage: obj.forward(x) and returns: x