code: def forward(self, m: torch.Tensor) -> torch.Tensor:
      n_context = m.shape[1]
      n_query_pad = (
          self.attn_fn.ctx_blks_q * self.attn_fn.block_size - n_context
      )
      n_key_pad = (
          self.attn_fn.ctx_blks_k * self.attn_fn.block_size - n_context
      )
      assert n_query_pad >= 0
      assert n_key_pad >= 0
  
      r = m
      r = self.ln(r)
      q, k, v = self.f_q(r), self.f_k(r), self.f_v(r)
  
      if n_query_pad != 0:
          q = F.pad(q, (0, 0, 0, n_query_pad))
  
      if n_key_pad != 0:
          k = F.pad(k, (0, 0, 0, n_key_pad))
          v = F.pad(v, (0, 0, 0, n_key_pad))
  
      q = q.view(
          [q.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]
      ).permute((0, 2, 1, 3))
      k = k.view(
          [k.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]
      ).permute((0, 2, 1, 3))
      v = v.view(
          [v.shape[0], -1, self.attn_fn.n_heads, self.n_head_state]
      ).permute((0, 2, 1, 3))
      w = torch.einsum(
          "bhcd,bhkd->bhck",
          q * math.sqrt(self.qk_scale),
          k * math.sqrt(self.qk_scale),
      )
  
      if hasattr(self.attn_fn, "pytorch_attn_bias"):
          bias = self.attn_fn.pytorch_attn_bias
          assert len(bias.shape) in {2, 3}
  
          if len(bias.shape) == 2:
              w = torch.softmax(
                  w + self.attn_fn.pytorch_attn_bias[None, None], dim=-1
              )
          elif len(bias.shape) == 3:
              w = torch.softmax(
                  w + self.attn_fn.pytorch_attn_bias[None], dim=-1
              )
      else:
          w = torch.softmax(w, dim=-1)
  
      r = torch.einsum("bhck,bhkd->bhcd", w, v)
      r = r.permute((0, 2, 1, 3)).reshape(
          (r.shape[0], -1, self.n_state)
      )
  
      if n_query_pad != 0:
          r = r[:, :-n_query_pad]
  
      assert r.shape[1] == n_context
  
      r = self.f_c(r)
      return m + r
  
imports: import math
  from collections import OrderedDict
  from typing import List, Optional, Tuple, cast
  import attr
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .attention import AttentionInfo, DenseAttentionMask, DenseCausalAttentionMask, make_full_layout, to_attention_info
  from .utils import Affine, LayerNorm, zero_key_bias_grad
label_desc: Use image node_id 2 for calling the AttentionResblock obj's underlying member method code with example usage: obj.forward(m) and returns: unknown