code: def forward(self, x, past_kv=None, use_cache=False):
      (
          B,
          T,
          C,
      ) = (
          x.size()
      )  # batch size, sequence length, embedding dimensionality (n_embd)
  
      # calculate query, key, values for all heads in batch and move head forward to be the batch dim
      q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
      k = k.view(B, T, self.n_head, C // self.n_head).transpose(
          1, 2
      )  # (B, nh, T, hs)
      q = q.view(B, T, self.n_head, C // self.n_head).transpose(
          1, 2
      )  # (B, nh, T, hs)
      v = v.view(B, T, self.n_head, C // self.n_head).transpose(
          1, 2
      )  # (B, nh, T, hs)
  
      if past_kv is not None:
          past_key = past_kv[0]
          past_value = past_kv[1]
          k = torch.cat((past_key, k), dim=-2)
          v = torch.cat((past_value, v), dim=-2)
  
      FULL_T = k.shape[-2]
  
      if use_cache is True:
          present = (k, v)
      else:
          present = None
  
      # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
      if self.flash:
          # efficient attention using Flash Attention CUDA kernels
          if past_kv is not None:
              # When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
              # the query for the last token. scaled_dot_product_attention interprets this as the first token in the
              # sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so
              # to work around this we set is_causal=False.
              is_causal = False
          else:
              is_causal = True
  
          # efficient attention using Flash Attention CUDA kernels
          y = torch.nn.functional.scaled_dot_product_attention(
              q, k, v, dropout_p=self.dropout, is_causal=is_causal
          )
      else:
          # manual implementation of attention
          att = (q @ k.transpose(-2, -1)) * (
              1.0 / math.sqrt(k.size(-1))
          )
          att = att.masked_fill(
              self.bias[:, :, FULL_T - T : FULL_T, :FULL_T] == 0,
              float("-inf"),
          )
          att = F.softmax(att, dim=-1)
          att = self.attn_dropout(att)
          y = (
              att @ v
          )  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
      y = (
          y.transpose(1, 2).contiguous().view(B, T, C)
      )  # re-assemble all head outputs side by side
  
      # output projection
      y = self.resid_dropout(self.c_proj(y))
      return (y, present)
  
imports: import math
  from dataclasses import dataclass
  import torch
  from coqpit import Coqpit
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the CausalSelfAttention obj's underlying member method code with example usage: obj.forward(x, past_kv, use_cache) and returns: y,  present