code: def __call__(
      self,
      hidden_states,
      attention_mask=None,
      position_ids=None,
      deterministic: bool = True,
      init_cache: bool = False,
      output_attentions: bool = False,
  ):
      residual = hidden_states
      hidden_states = self.input_layernorm(hidden_states)
      outputs = self.self_attn(
          hidden_states,
          attention_mask=attention_mask,
          position_ids=position_ids,
          deterministic=deterministic,
          init_cache=init_cache,
          output_attentions=output_attentions,
      )
      # residual connection
      attn_output = outputs[0]
      hidden_states = residual + attn_output
  
      residual = hidden_states
      hidden_states = self.post_attention_layernorm(hidden_states)
      hidden_states = self.mlp(hidden_states)
      # residual connection
      hidden_states = residual + hidden_states
  
      return (hidden_states,) + outputs[1:]
  
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 FlaxLlamaDecoderLayer obj's underlying member method code with example usage: obj.__call__(hidden_states, attention_mask, position_ids, deterministic, init_cache, output_attentions) and returns: unknown