code: def prepare_inputs_for_generation(
      self,
      input_ids,
      max_length,
      attention_mask: Optional[jax.Array] = None,
  ):
      # initializing the cache
      batch_size, seq_length = input_ids.shape
  
      past_key_values = self.init_cache(batch_size, max_length)
      # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
      # But since Llama uses a causal mask, those positions are masked anyways.
      # Thus we can create a single static attention_mask here, which is more efficient for compilation
      extended_attention_mask = jnp.ones(
          (batch_size, max_length), dtype="i4"
      )
      if attention_mask is not None:
          position_ids = attention_mask.cumsum(axis=-1) - 1
          extended_attention_mask = lax.dynamic_update_slice(
              extended_attention_mask, attention_mask, (0, 0)
          )
      else:
          position_ids = jnp.broadcast_to(
              jnp.arange(seq_length, dtype="i4")[None, :],
              (batch_size, seq_length),
          )
  
      return {
          "past_key_values": past_key_values,
          "attention_mask": extended_attention_mask,
          "position_ids": position_ids,
      }
  
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 1 for calling the FlaxLlamaForCausalLM obj's underlying member method code with example usage: obj.prepare_inputs_for_generation(input_ids, max_length, attention_mask) and returns: dict