code: def __call__(
      self,
      hidden_states,
      attention_mask,
      position_ids,
      deterministic: bool = True,
      init_cache: bool = False,
      output_attentions: bool = False,
  ):
      query = self.q_proj(hidden_states)
      key = self.k_proj(hidden_states)
      value = self.v_proj(hidden_states)
  
      query = self._split_heads(query)
      key = self._split_heads(key)
      value = self._split_heads(value)
  
      key, query = self.rotary_emb(key, query, position_ids)
  
      query_length, key_length = query.shape[1], key.shape[1]
  
      if self.has_variable("cache", "cached_key"):
          mask_shift = self.variables["cache"]["cache_index"]
          max_decoder_length = self.variables["cache"][
              "cached_key"
          ].shape[1]
          causal_mask = lax.dynamic_slice(
              self.causal_mask,
              (0, 0, mask_shift, 0),
              (1, 1, query_length, max_decoder_length),
          )
      else:
          causal_mask = self.causal_mask[
              :, :, :query_length, :key_length
          ]
  
      batch_size = hidden_states.shape[0]
      causal_mask = jnp.broadcast_to(
          causal_mask, (batch_size,) + causal_mask.shape[1:]
      )
  
      attention_mask = jnp.broadcast_to(
          jnp.expand_dims(attention_mask, axis=(-3, -2)),
          causal_mask.shape,
      )
      attention_mask = combine_masks(attention_mask, causal_mask)
  
      dropout_rng = None
      if not deterministic and self.config.attention_dropout > 0.0:
          dropout_rng = self.make_rng("dropout")
  
      # During fast autoregressive decoding, we feed one position at a time,
      # and cache the keys and values step by step.
      if self.has_variable("cache", "cached_key") or init_cache:
          key, value, attention_mask = self._concatenate_to_cache(
              key, value, query, attention_mask
          )
  
      # transform boolean mask into float mask
      attention_bias = lax.select(
          attention_mask > 0,
          jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
          jnp.full(
              attention_mask.shape, jnp.finfo(self.dtype).min
          ).astype(self.dtype),
      )
  
      # usual dot product attention
      attention_dtype = (
          jnp.float32 if self.attention_softmax_in_fp32 else self.dtype
      )
      attn_weights = dot_product_attention_weights(
          query,
          key,
          bias=attention_bias,
          dropout_rng=dropout_rng,
          dropout_rate=self.config.attention_dropout,
          deterministic=deterministic,
          dtype=attention_dtype,
      )
  
      if self.attention_softmax_in_fp32:
          attn_weights = attn_weights.astype(self.dtype)
  
      attn_output = jnp.einsum(
          "...hqk,...khd->...qhd", attn_weights, value
      )
      attn_output = self._merge_heads(attn_output)
      attn_output = self.o_proj(attn_output)
  
      outputs = (
          (attn_output, attn_weights)
          if output_attentions
          else (attn_output,)
      )
      return outputs
  
imports: from functools import partial
  from typing import Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import numpy
  from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
  from flax.linen import combine_masks, make_causal_mask
  from flax.linen.attention import dot_product_attention_weights
  from flax.traverse_util import flatten_dict, unflatten_dict
  from jax import lax
  from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
  from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_llama import LlamaConfig
label_desc: Use image node_id 5 for calling the FlaxLlamaAttention obj's underlying member method code with example usage: obj.__call__(hidden_states, attention_mask, position_ids, deterministic, init_cache, output_attentions) and returns: outputs