code: def setup_caches(self, max_batch_size, max_seq_length):
      if (
          self.max_seq_length >= max_seq_length
          and self.max_batch_size >= max_batch_size
      ):
          return
      head_dim = self.config.dim // self.config.n_head
      max_seq_length = find_multiple(max_seq_length, 8)
      self.max_seq_length = max_seq_length
      self.max_batch_size = max_batch_size
      for b in self.layers:
          b.attention.kv_cache = KVCache(
              max_batch_size,
              max_seq_length,
              self.config.n_local_heads,
              head_dim,
          )
  
      self.freqs_cis = precompute_freqs_cis(
          self.config.block_size,
          self.config.dim // self.config.n_head,
          self.config.rope_base,
      )
      self.causal_mask = torch.tril(
          torch.ones(
              self.max_seq_length, self.max_seq_length, dtype=torch.bool
          )
      )
  
imports: from dataclasses import dataclass
  from typing import Optional
  import torch
  import torch.nn
  from torch import Tensor
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the Transformer obj's underlying member method code with example usage: obj.setup_caches(max_batch_size, max_seq_length) without return types