class_docstr: An attention block that allows spatial positions to attend to each other.
code: def __init__(
      self,
      channels,
      num_heads=1,
      num_head_channels=-1,
      out_channels=None,
      do_activation=False,
  ):
      super().__init__()
      self.channels = channels
      out_channels = channels if out_channels is None else out_channels
      self.do_activation = do_activation
      if num_head_channels == -1:
          self.num_heads = num_heads
      else:
          assert (
              channels % num_head_channels == 0
          ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
          self.num_heads = channels // num_head_channels
      self.norm = normalization(channels)
      self.qkv = conv_nd(1, channels, out_channels * 3, 1)
      self.attention = QKVAttention(self.num_heads)
  
      self.x_proj = (
          nn.Identity()
          if out_channels == channels
          else conv_nd(1, channels, out_channels, 1)
      )
      self.proj_out = zero_module(
          conv_nd(1, out_channels, out_channels, 1)
      )
  
imports: import math
  import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 to create a new AttentionBlock object from inherited base classes: nn with example: obj = AttentionBlock(channels, num_heads, num_head_channels, out_channels, do_activation)