code: def __call__(
      self,
      hidden_states,
      attention_mask,
      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,
      output_hidden_states: bool = False,
      return_dict: bool = True,
  ):
      # decoder layers
      all_hidden_states = () if output_hidden_states else None
      all_self_attns = () if output_attentions else None
      all_cross_attentions = (
          ()
          if (output_attentions and encoder_hidden_states is not None)
          else None
      )
  
      for decoder_layer in self.layers:
          if output_hidden_states:
              all_hidden_states += (hidden_states,)
              # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
          dropout_probability = random.uniform(0, 1)
          if not deterministic and (
              dropout_probability < self.layerdrop
          ):
              layer_outputs = (None, None, None)
          else:
              layer_outputs = decoder_layer(
                  hidden_states,
                  attention_mask=attention_mask,
                  encoder_hidden_states=encoder_hidden_states,
                  encoder_attention_mask=encoder_attention_mask,
                  init_cache=init_cache,
                  output_attentions=output_attentions,
                  deterministic=deterministic,
              )
  
          hidden_states = layer_outputs[0]
          if output_attentions:
              all_self_attns += (layer_outputs[1],)
  
              if encoder_hidden_states is not None:
                  all_cross_attentions += (layer_outputs[2],)
  
      # add hidden states from the last decoder layer
      if output_hidden_states:
          all_hidden_states += (hidden_states,)
  
      outputs = (
          hidden_states,
          all_hidden_states,
          all_self_attns,
          all_cross_attentions,
      )
  
      if not return_dict:
          return tuple(v for v in outputs if v is not None)
  
      return FlaxBaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=hidden_states,
          hidden_states=all_hidden_states,
          attentions=all_self_attns,
          cross_attentions=all_cross_attentions,
      )
  
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 FlaxXGLMDecoderLayerCollection 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, output_hidden_states, return_dict) and returns: FlaxBaseModelOutputWithPastAndCrossAttentions, tuple