code: def __call__(
      self,
      hidden_states: jnp.ndarray,
      attention_mask: jnp.ndarray,
      encoder_hidden_states: Optional[jnp.ndarray] = None,
      encoder_attention_mask: Optional[jnp.ndarray] = None,
      init_cache: bool = False,
      output_attentions: bool = True,
      deterministic: bool = True,
  ) -> Tuple[jnp.ndarray]:
      residual = hidden_states
      hidden_states = self.self_attn_layer_norm(hidden_states)
  
      # Self Attention
      hidden_states, self_attn_weights = self.self_attn(
          hidden_states=hidden_states,
          attention_mask=attention_mask,
          init_cache=init_cache,
      )
      hidden_states = self.dropout_layer(
          hidden_states, deterministic=deterministic
      )
      hidden_states = residual + hidden_states
  
      # Cross-Attention Block
      cross_attn_weights = None
      if encoder_hidden_states is not None:
          residual = hidden_states
  
          hidden_states = self.encoder_attn_layer_norm(hidden_states)
          hidden_states, cross_attn_weights = self.encoder_attn(
              hidden_states=hidden_states,
              key_value_states=encoder_hidden_states,
              attention_mask=encoder_attention_mask,
          )
          hidden_states = self.dropout_layer(
              hidden_states, deterministic=deterministic
          )
          hidden_states = residual + hidden_states
  
      # Fully Connected
      residual = hidden_states
      hidden_states = self.final_layer_norm(hidden_states)
      hidden_states = self.activation_fn(self.fc1(hidden_states))
      hidden_states = self.activation_dropout_layer(
          hidden_states, deterministic=deterministic
      )
      hidden_states = self.fc2(hidden_states)
      hidden_states = self.dropout_layer(
          hidden_states, deterministic=deterministic
      )
      hidden_states = residual + hidden_states
  
      outputs = (hidden_states,)
  
      if output_attentions:
          outputs += (self_attn_weights, cross_attn_weights)
  
      return outputs
  
imports: import math
  import random
  from functools import partial
  from typing import Optional, Tuple
  import numpy
  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 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_xglm import XGLMConfig
label_desc: Use image node_id 2 for calling the FlaxXGLMDecoderLayer obj's underlying member method code with example usage: obj.__call__(hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, init_cache, output_attentions, deterministic) and returns: outputs