code: def __call__(
      self,
      hidden_states,
      attention_mask=None,
      position_ids=None,
      deterministic: bool = True,
      init_cache: bool = False,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = False,
  ):
      all_attentions = () if output_attentions else None
      all_hidden_states = () if output_hidden_states else None
  
      for block in self.blocks:
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
          layer_outputs = block(
              hidden_states,
              attention_mask=attention_mask,
              position_ids=position_ids,
              deterministic=deterministic,
              init_cache=init_cache,
              output_attentions=output_attentions,
          )
          hidden_states = layer_outputs[0]
  
          if output_attentions:
              all_attentions += (layer_outputs[1],)
  
      # this contains possible `None` values - `FlaxLlamaModule` will filter them out
      outputs = (hidden_states, all_hidden_states, all_attentions)
  
      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 2 for calling the FlaxLlamaLayerCollection obj's underlying member method code with example usage: obj.__call__(hidden_states, attention_mask, position_ids, deterministic, init_cache, output_attentions, output_hidden_states, return_dict) and returns: outputs