code: def full_block(
      layer_input,
      attention_head_size,
      batch_size,
      seq_length,
      attention_mask=None,
      hidden_size=768,
      num_attention_heads=12,
      intermediate_size=3072,
      intermediate_act_fn=gelu,
      hidden_dropout_prob=0.1,
      attention_probs_dropout_prob=0.1,
      initializer_range=0.02,
  ):
      with tf.compat.v1.variable_scope("attention"):
          attention_heads = []
          with tf.compat.v1.variable_scope("self"):
              attention_head = attention_layer(
                  from_tensor=layer_input,
                  to_tensor=layer_input,
                  attention_mask=attention_mask,
                  num_attention_heads=num_attention_heads,
                  size_per_head=attention_head_size,
                  attention_probs_dropout_prob=attention_probs_dropout_prob,
                  initializer_range=initializer_range,
                  do_return_2d_tensor=True,
                  batch_size=batch_size,
                  from_seq_length=seq_length,
                  to_seq_length=seq_length,
              )
              attention_heads.append(attention_head)
  
          if len(attention_heads) == 1:
              attention_output = attention_heads[0]
          else:
              # In the case where we have other sequences, we just concatenate
              # them to the self-attention head before the projection.
              attention_output = tf.concat(attention_heads, axis=-1)
          # Run a linear projection of `hidden_size` then add a residual
          # with `layer_input`.
          with tf.compat.v1.variable_scope("output"):
              attention_output = tf.compat.v1.layers.dense(
                  attention_output,
                  hidden_size,
                  kernel_initializer=create_initializer(
                      initializer_range
                  ),
              )
              attention_output = dropout(
                  attention_output, hidden_dropout_prob
              )
              attention_output = layer_norm(
                  attention_output + layer_input
              )
  
      # The activation is only applied to the "intermediate" hidden layer.
      with tf.compat.v1.variable_scope("intermediate"):
          intermediate_output = tf.compat.v1.layers.dense(
              attention_output,
              intermediate_size,
              activation=intermediate_act_fn,
              kernel_initializer=create_initializer(initializer_range),
          )
  
      # Down-project back to `hidden_size` then add the residual.
      with tf.compat.v1.variable_scope("output"):
          layer_output = tf.compat.v1.layers.dense(
              intermediate_output,
              hidden_size,
              kernel_initializer=create_initializer(initializer_range),
          )
          layer_output = dropout(layer_output, hidden_dropout_prob)
          layer_output = layer_norm(layer_output + attention_output)
          return layer_output
  
imports: import copy
  import json
  import math
  import numpy
  import tensorflow
  import functools
  from transformers.activations_tf import gelu
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.nn.auxiliary import embed_position
label_desc: Use image node_id 14 for calling a global function with example usage: full_block(layer_input, attention_head_size, batch_size, seq_length, attention_mask, hidden_size, num_attention_heads, intermediate_size, intermediate_act_fn, hidden_dropout_prob, attention_probs_dropout_prob, initializer_range) and returns: layer_output