code: def __init__(self, config):
      super().__init__()
      assert config.n_embd % config.n_head == 0
      # key, query, value projections for all heads, but in a batch
      self.c_attn = nn.Linear(
          config.n_embd, 3 * config.n_embd, bias=config.bias
      )
      # output projection
      self.c_proj = nn.Linear(
          config.n_embd, config.n_embd, bias=config.bias
      )
      # regularization
      self.attn_dropout = nn.Dropout(config.dropout)
      self.resid_dropout = nn.Dropout(config.dropout)
      self.n_head = config.n_head
      self.n_embd = config.n_embd
      self.dropout = config.dropout
      # flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
      self.flash = hasattr(
          torch.nn.functional, "scaled_dot_product_attention"
      )
      if not self.flash:
          # print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
          # causal mask to ensure that attention is only applied to the left in the input sequence
          self.register_buffer(
              "bias",
              torch.tril(
                  torch.ones(config.block_size, config.block_size)
              ).view(1, 1, config.block_size, config.block_size),
          )
  
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 1 to create a new CausalSelfAttention object from inherited base classes: nn with example: obj = CausalSelfAttention(config)