docstr: This function takes projected key, value states from a single input token and concatenates the states to cached
  states from previous steps. This function is slighly adapted from the official Flax repository:
  https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
docstr_tok: ["This","function","takes","projected","key",",","value","states","from","a","single","input","token","and","concatenates","the","states","to","cached","states","from","previous","steps",".","This","function","is","slighly","adapted","from","the","official","Flax","repository",":","https",":","\/\/github.com\/google\/flax\/blob\/491ce18759622506588784b4fca0e4bf05f8c8cd\/flax\/linen\/attention.py","#","L252"]
code: def _concatenate_to_cache(self, key, value, query, attention_mask):
      """
      This function takes projected key, value states from a single input token and concatenates the states to cached
      states from previous steps. This function is slighly adapted from the official Flax repository:
      https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
      """
      # detect if we're initializing by absence of existing cache data.
      is_initialized = self.has_variable("cache", "cached_key")
      cached_key = self.variable(
          "cache", "cached_key", jnp.zeros, key.shape, key.dtype
      )
      cached_value = self.variable(
          "cache", "cached_value", jnp.zeros, value.shape, value.dtype
      )
      cache_index = self.variable(
          "cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)
      )
  
      if is_initialized:
          (
              *batch_dims,
              max_length,
              num_heads,
              depth_per_head,
          ) = cached_key.value.shape
          # update key, value caches with our new 1d spatial slices
          cur_index = cache_index.value
          indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
          key = lax.dynamic_update_slice(cached_key.value, key, indices)
          value = lax.dynamic_update_slice(
              cached_value.value, value, indices
          )
          cached_key.value = key
          cached_value.value = value
          num_updated_cache_vectors = query.shape[1]
          cache_index.value = (
              cache_index.value + num_updated_cache_vectors
          )
          # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
          pad_mask = jnp.broadcast_to(
              jnp.arange(max_length)
              < cur_index + num_updated_cache_vectors,
              tuple(batch_dims)
              + (1, num_updated_cache_vectors, max_length),
          )
          attention_mask = combine_masks(pad_mask, attention_mask)
      return key, value, attention_mask
  
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 4 for calling the FlaxLlamaAttention obj's underlying member method code with example usage: obj._concatenate_to_cache(key, value, query, attention_mask) and returns: key,  value,  attention_mask