code: def setup(self):
      config = self.config
      self.embed_dim = config.hidden_size
      self.num_heads = config.num_attention_heads
      self.head_dim = self.embed_dim // self.num_heads
  
      if self.is_cross_attention:
          self.c_attn = FlaxConv1D(2 * self.embed_dim, dtype=self.dtype)
          self.q_attn = FlaxConv1D(self.embed_dim, dtype=self.dtype)
      else:
          self.c_attn = FlaxConv1D(3 * self.embed_dim, dtype=self.dtype)
      self.c_proj = FlaxConv1D(self.embed_dim, dtype=self.dtype)
  
      self.resid_dropout = nn.Dropout(rate=config.resid_pdrop)
  
      if self.causal:
          self.causal_mask = make_causal_mask(
              jnp.ones(
                  (1, config.max_position_embeddings), dtype="bool"
              ),
              dtype="bool",
          )
  
imports: from typing import Any, 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 ...modeling_flax_outputs import FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions
  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_gpt2 import GPT2Config
label_desc: Use image node_id 1 for calling the FlaxGPT2Attention obj's underlying member method code with example usage: obj.setup() without return types