code: def transformer_patches(patches):
      # TODO: Make this a context manager or something to limit the scope of these patches.
      if patches is None:
          return
      if "longformer" in patches:
          call_orig = TFLongformerSelfAttention.call
  
          def new_call_longformer(self, inputs, training=False):
              attention_mask = inputs[1]
              orig_ne = attention_mask.__class__.__ne__
  
              def custom_ne(a, b):
                  if isinstance(b, int):  # a is attention_mask:
                      return tf.math.not_equal(a, b)
                  return orig_ne(a, b)
  
              attention_mask.__class__.__ne__ = custom_ne
              output = call_orig(self, inputs=inputs, training=training)
              attention_mask.__class__.__ne__ = orig_ne
              return output
  
          TFLongformerSelfAttention.call = new_call_longformer
  
      if "t5" in patches:
  
          @tf_utils.shape_type_conversion
          def build(self, input_shape=None):
              try:
                  weight_name = self.weight_name
              except AttributeError:
                  weight_name = "embeddings"
  
              self.embeddings = self.add_weight(
                  shape=(self.input_dim, self.output_dim),
                  initializer=self.embeddings_initializer,
                  name=weight_name,
                  regularizer=self.embeddings_regularizer,
                  constraint=self.embeddings_constraint,
                  experimental_autocast=False,
              )
              self.built = True
  
          Embedding.build = build
  
imports: import h5py
  import numpy
  import joblib
  import os
  import tqdl
  import logging
  import tensorflow
  import unicodedata
  import string
  from tensorflow.python.keras.saving.hdf5_format import load_attributes_from_hdf5_group
  from finetune.util.download import FINETUNE_BASE_FOLDER
  from finetune.util.shapes import lengths_from_eos_idx
  from finetune.util.tokenization import normalize_nfkc, WEIRD_SPM_CHAR
  from finetune.encoding.input_encoder import BaseEncoder
  from finetune.encoding.input_encoder import EncodedOutput
  from finetune.base_models import SourceModel
  from finetune.optimizers.recompute_grads import recompute_grads_w_kwargs
  from finetune.util.featurizer_fusion import fused_featurizer
  from tensorflow.python.util import tf_inspect
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.keras.layers import Embedding
  from transformers.models.longformer.modeling_tf_longformer import TFLongformerSelfAttention
label_desc: Use image node_id 1 for calling a global function with example usage: transformer_patches(patches) and returns: output, orig_ne, tf