code: def __call__(
      self,
      x: Tensor,
      start_pos: Union[Variable, int],
      freqs_cis: Tensor,
      mask: Optional[Tensor],
  ) -> Tensor:
      x = x.half()
      xq, xk, xv = (
          self.wq(x).half(),
          self.wk(x).half(),
          self.wv(x).half(),
      )
      xq = xq.reshape(
          xq.shape[0], xq.shape[1], self.n_heads, self.head_dim
      )
      xk = xk.reshape(
          xk.shape[0], xk.shape[1], self.n_kv_heads, self.head_dim
      )
      xv = xv.reshape(
          xv.shape[0], xv.shape[1], self.n_kv_heads, self.head_dim
      )
  
      xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
      bsz, seqlen, _, _ = xq.shape
  
      # create kv cache
      if not hasattr(self, "cache_k"):
          self.cache_k = Tensor.zeros(
              bsz,
              self.max_context,
              self.n_kv_heads,
              self.head_dim,
              dtype=x.dtype,
          ).contiguous()
          self.cache_v = Tensor.zeros(
              bsz,
              self.max_context,
              self.n_kv_heads,
              self.head_dim,
              dtype=x.dtype,
          ).contiguous()
          if isinstance(x.device, tuple):
              # TODO: instead of specifying how to shard, it can follow how xk and xv are being sharded
              self.cache_k.shard_((xk.device), axis=None)
              self.cache_v.shard_((xv.device), axis=None)
  
      # HACK: without contiguous, the conversation mode is broken and the cache is not updated
      keys = (
          self.cache_k.shrink((None, (0, start_pos), None, None))
          .cat(xk, dim=1)
          .contiguous()
          if start_pos > 0
          else xk
      )
      values = (
          self.cache_v.shrink((None, (0, start_pos), None, None))
          .cat(xv, dim=1)
          .contiguous()
          if start_pos > 0
          else xv
      )
  
      # update the cache
      assert (
          keys.dtype == self.cache_k.dtype
          and values.dtype == self.cache_v.dtype
      ), f"{keys.dtype=}, {values.dtype=}, {self.cache_k.dtype=}, {self.cache_v.dtype=}"
      self.cache_k.assign(
          keys.pad(
              (
                  None,
                  (0, self.max_context - start_pos - seqlen),
                  None,
                  None,
              )
          ).contiguous()
      ).realize()
      self.cache_v.assign(
          values.pad(
              (
                  None,
                  (0, self.max_context - start_pos - seqlen),
                  None,
                  None,
              )
          ).contiguous()
      ).realize()
      keys, values = repeat_kv(keys, self.n_rep), repeat_kv(
          values, self.n_rep
      )
      xq, keys, values = (
          xq.transpose(1, 2),
          keys.transpose(1, 2),
          values.transpose(1, 2),
      )
      attn = xq.scaled_dot_product_attention(
          keys, values, mask
      ).transpose(1, 2)
      attn = attn.reshape(bsz, seqlen, -1)
      return self.wo(attn)
  
imports: from typing import Tuple, Union, Optional, Dict
  from tinygrad import Tensor, Variable, TinyJit, dtypes, nn, Device
  from tinygrad.helpers import getenv
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.__call__(x, start_pos, freqs_cis, mask) and returns: self