code: def __call__(
      self,
      hidden_states,
      attention_mask=None,
      encoder_hidden_states: Optional[jnp.ndarray] = None,
      encoder_attention_mask: Optional[jnp.ndarray] = None,
      deterministic: bool = True,
      init_cache: bool = False,
      output_attentions: bool = False,
  ):
      residual = hidden_states
      hidden_states = self.ln_1(hidden_states)
      attn_outputs = self.attn(
          hidden_states,
          attention_mask=attention_mask,
          deterministic=deterministic,
          init_cache=init_cache,
          output_attentions=output_attentions,
      )
      # residual connection
      attn_output = attn_outputs[0]  # output_attn: a, (attentions)
      outputs = attn_outputs[1:]
      # residual connection
      hidden_states = attn_output + residual
  
      # Cross-Attention Block
      if encoder_hidden_states is not None:
          # add one self-attention block for cross-attention
          if not hasattr(self, "crossattention"):
              raise ValueError(
                  f"If `encoder_hidden_states` are passed, {self} has to be instantiated with "
                  "cross-attention layers by setting `config.add_cross_attention=True`"
              )
          residual = hidden_states
          hidden_states = self.ln_cross_attn(hidden_states)
          cross_attn_outputs = self.crossattention(
              hidden_states,
              key_value_states=encoder_hidden_states,
              attention_mask=encoder_attention_mask,
              deterministic=deterministic,
              output_attentions=output_attentions,
          )
          attn_output = cross_attn_outputs[0]
          # residual connection
          hidden_states = residual + attn_output
          outputs = (
              outputs + cross_attn_outputs[1:]
          )  # add cross attentions if we output attention weights
  
      residual = hidden_states
      hidden_states = self.ln_2(hidden_states)
      feed_forward_hidden_states = self.mlp(
          hidden_states, deterministic=deterministic
      )
      # residual connection
      hidden_states = residual + feed_forward_hidden_states
  
      outputs = (hidden_states,) + outputs
  
      return outputs
  
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 2 for calling the FlaxGPT2Block obj's underlying member method code with example usage: obj.__call__(hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic, init_cache, output_attentions) and returns: outputs