code: def __call__(
      self,
      input_ids,
      attention_mask,
      position_ids,
      init_cache: bool = False,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = True,
      deterministic: bool = True,
  ):
      input_shape = input_ids.shape
      input_ids = input_ids.reshape(-1, input_shape[-1])
  
      inputs_embeds = self.embed_tokens(input_ids)
      if self.project_in is not None:
          inputs_embeds = self.project_in(inputs_embeds)
  
      positions = self.embed_positions(position_ids)
  
      hidden_states = inputs_embeds + positions
  
      hidden_state, all_hidden_states, attentions = self.layers(
          hidden_states,
          attention_mask,
          deterministic=deterministic,
          init_cache=init_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
      )
  
      if self.final_layer_norm is not None:
          hidden_state = self.final_layer_norm(hidden_state)
  
      if self.project_out is not None:
          hidden_state = self.project_out(hidden_state)
  
      if output_hidden_states:
          all_hidden_states += (hidden_state,)
  
      outputs = [hidden_state, all_hidden_states, attentions]
  
      if not return_dict:
          return tuple(v for v in outputs if v is not None)
  
      return FlaxBaseModelOutput(
          last_hidden_state=hidden_state,
          hidden_states=all_hidden_states,
          attentions=attentions,
      )
  
imports: from functools import partial
  from typing import Optional, Tuple
  import flax.linen
  import jax
  import jax.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 jax.random import PRNGKey
  from ...modeling_flax_outputs import FlaxBaseModelOutput, FlaxMaskedLMOutput
  from ...modeling_flax_utils import ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring
  from ...utils import add_start_docstrings, logging
  from .configuration_opt import OPTConfig
label_desc: Use image node_id 2 for calling the FlaxOPTDecoder obj's underlying member method code with example usage: obj.__call__(input_ids, attention_mask, position_ids, init_cache, output_attentions, output_hidden_states, return_dict, deterministic) and returns: FlaxBaseModelOutput, tuple