code: def __call__(
      self,
      input_ids,
      attention_mask,
      position_ids,
      encoder_hidden_states: Optional[jnp.ndarray] = None,
      encoder_attention_mask: Optional[jnp.ndarray] = None,
      init_cache: bool = False,
      output_attentions: bool = False,
      output_hidden_states: bool = False,
      return_dict: bool = True,
      deterministic: bool = True,
  ):
      input_shape = input_ids.shape
      input_ids = input_ids.reshape(-1, input_shape[-1])
  
      inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
  
      # embed positions
      position_ids = position_ids + self.offset
      positions = jnp.take(self.embed_positions, position_ids, axis=0)
  
      hidden_states = inputs_embeds + positions
      hidden_states = self.dropout_layer(
          hidden_states, deterministic=deterministic
      )
  
      outputs = self.layers(
          hidden_states,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
          deterministic=deterministic,
          init_cache=init_cache,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
      last_hidden_states = outputs[0]
      last_hidden_states = self.layer_norm(last_hidden_states)
  
      hidden_states = None
      if output_hidden_states:
          hidden_states = outputs[1]
          hidden_states = hidden_states[:-1] + (last_hidden_states,)
  
      if not return_dict:
          outputs = (last_hidden_states, hidden_states) + (
              outputs[2:] if output_hidden_states else outputs[1:]
          )
          return tuple(v for v in outputs if v is not None)
  
      return FlaxBaseModelOutputWithPastAndCrossAttentions(
          last_hidden_state=last_hidden_states,
          hidden_states=hidden_states,
          attentions=outputs.attentions,
          cross_attentions=outputs.cross_attentions,
      )
  
imports: import math
  import random
  from functools import partial
  from typing import Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import 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 FlaxXGLMModule obj's underlying member method code with example usage: obj.__call__(input_ids, attention_mask, position_ids, encoder_hidden_states, encoder_attention_mask, init_cache, output_attentions, output_hidden_states, return_dict, deterministic) and returns: FlaxBaseModelOutputWithPastAndCrossAttentions, tuple