code: def setup(self) -> None:
      self.embed_dim = self.config.hidden_size
      self.self_attn = FlaxOPTAttention(
          config=self.config,
          embed_dim=self.embed_dim,
          num_heads=self.config.num_attention_heads,
          dropout=self.config.attention_dropout,
          causal=True,
          dtype=self.dtype,
      )
      self.do_layer_norm_before = self.config.do_layer_norm_before
      self.dropout_layer = nn.Dropout(rate=self.config.dropout)
      self.activation_fn = ACT2FN[self.config.activation_function]
  
      self.self_attn_layer_norm = nn.LayerNorm(
          dtype=self.dtype, epsilon=1e-05
      )
      self.fc1 = nn.Dense(
          self.config.ffn_dim,
          dtype=self.dtype,
          kernel_init=jax.nn.initializers.normal(self.config.init_std),
      )
      self.fc2 = nn.Dense(
          self.embed_dim,
          dtype=self.dtype,
          kernel_init=jax.nn.initializers.normal(self.config.init_std),
      )
      self.final_layer_norm = nn.LayerNorm(
          dtype=self.dtype, epsilon=1e-05
      )
  
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 1 for calling the FlaxOPTDecoderLayer obj's underlying member method code with example usage: obj.setup() without return types