code: import re
  from collections import defaultdict
  from multiprocessing import Process, Queue, cpu_count
  from typing import Any, Dict, Iterable, List, NamedTuple, Set, Iterator
  
  import tensorflow as tf
  import numpy as np
  from dpu_utils.utils import RichPath
  from dpu_utils.codeutils import split_identifier_into_parts, get_language_keywords
  
  from .sparse_graph_task import Sparse_Graph_Task, DataFold, MinibatchData
  from utils import BIG_NUMBER
  
  
  ALPHABET = "abcdefghijklmnopqrstuvwxyz0123456789,;.!?:'\"/\\|_@#$%^&*~`+-=<>()[]{}"
  ALPHABET_DICT = {char: idx + 2 for (idx, char) in enumerate(ALPHABET)}  # "0" is PAD, "1" is UNK
  ALPHABET_DICT["PAD"] = 0
  ALPHABET_DICT["UNK"] = 1
  USES_SUBTOKEN_EDGE_NAME = "UsesSubtoken"
  SELF_LOOP_EDGE_NAME = "SelfLoop"
  BACKWARD_EDGE_TYPE_NAME_SUFFIX = "_Bkwd"
  __PROGRAM_GRAPH_EDGES_TYPES = ["Child", "NextToken", "LastUse", "LastWrite", "LastLexicalUse", "ComputedFrom",
                                 "GuardedByNegation", "GuardedBy", "FormalArgName", "ReturnsTo", USES_SUBTOKEN_EDGE_NAME]
  __PROGRAM_GRAPH_EDGES_TYPES_WITH_BKWD = \
      __PROGRAM_GRAPH_EDGES_TYPES + [edge_type_name + BACKWARD_EDGE_TYPE_NAME_SUFFIX
                                     for edge_type_name in __PROGRAM_GRAPH_EDGES_TYPES]
  PROGRAM_GRAPH_EDGES_TYPES_VOCAB = {edge_type_name: idx
                                     for idx, edge_type_name in enumerate(__PROGRAM_GRAPH_EDGES_TYPES_WITH_BKWD)}
  
  
  class GraphSample(NamedTuple):
      adjacency_lists: List[np.ndarray]
      type_to_node_to_num_incoming_edges: np.ndarray
      unique_labels_as_characters: np.ndarray
      node_labels_to_unique_labels: np.ndarray
      slot_node_id: int
      variable_candidate_nodes: np.ndarray
      variable_candidate_nodes_mask: np.ndarray
  
  
  def _add_per_subtoken_nodes(unsplittable_node_names: Set[str], graph_dict: Dict[str, Any]) -> None:
      graph_node_labels = graph_dict['NodeLabels']
      subtoken_to_using_nodes = defaultdict(set)
  
      max_used_node_id = 0
      for node_id, node_label in graph_node_labels.items():
          node_id = int(node_id)
          max_used_node_id = max(node_id, max_used_node_id)
  
          # Skip AST nodes and punctuation:
          if node_label in unsplittable_node_names:
              continue
  
          for subtoken in split_identifier_into_parts(node_label):
              if re.search('[a-zA-Z0-9]', subtoken):
                  subtoken_to_using_nodes[subtoken].add(node_id)
  
      subtoken_node_id = max_used_node_id
      new_edges = []
      for subtoken, using_nodes in subtoken_to_using_nodes.items():
          subtoken_node_id += 1
          graph_node_labels[str(subtoken_node_id)] = subtoken
          new_edges.extend([(using_node_id, subtoken_node_id)
                            for using_node_id in using_nodes])
  
      graph_dict['Edges'][USES_SUBTOKEN_EDGE_NAME] = new_edges
  
  
  def _load_single_sample(raw_sample: Dict[str, Any],
                          unsplittable_node_names: Set[str],
                          graph_node_label_max_num_chars: int,
                          max_variable_candidates: int = 5,
                          add_self_loop_edges: bool = False):
      _add_per_subtoken_nodes(unsplittable_node_names, raw_sample['ContextGraph'])
      num_nodes = len(raw_sample['ContextGraph']['NodeLabels'])
  
      node_label_chars = np.zeros(shape=(num_nodes, graph_node_label_max_num_chars),
                                  dtype=np.uint8)
      for (node, label) in raw_sample['ContextGraph']['NodeLabels'].items():
          for (char_idx, label_char) in enumerate(label[:graph_node_label_max_num_chars].lower()):
              node_label_chars[int(node), char_idx] = ALPHABET_DICT.get(label_char, 1)
      node_label_chars_unique, node_label_chars_indices = np.unique(node_label_chars,
                                                                    axis=0,
                                                                    return_inverse=True)
  
      # Split edges according to edge_type and count their numbers:
      num_edge_types = len(PROGRAM_GRAPH_EDGES_TYPES_VOCAB)
      adjacency_lists = [np.zeros((0, 2), dtype=np.int32) for _ in range(num_edge_types)]
      num_incoming_edges_per_type = np.zeros((num_edge_types, num_nodes), dtype=np.uint16)
      raw_edges = raw_sample['ContextGraph']['Edges']
      for e_type, e_type_edges in raw_edges.items():
          if len(e_type_edges) > 0:
              e_type_bkwd = e_type + BACKWARD_EDGE_TYPE_NAME_SUFFIX
              e_type_idx = PROGRAM_GRAPH_EDGES_TYPES_VOCAB[e_type]
              e_type_bkwd_idx = PROGRAM_GRAPH_EDGES_TYPES_VOCAB[e_type_bkwd]
  
              fwd_edges = np.array(e_type_edges, dtype=np.int32)
              bkwd_edges = np.flip(fwd_edges, axis=1)
  
              adjacency_lists[e_type_idx] = fwd_edges
              adjacency_lists[e_type_bkwd_idx] = bkwd_edges
              num_incoming_edges_per_type[e_type_idx, :] = \
                  np.bincount(adjacency_lists[e_type_idx][:, 1], minlength=num_nodes)
              num_incoming_edges_per_type[e_type_bkwd_idx, :] = \
                  np.bincount(adjacency_lists[e_type_bkwd_idx][:, 1], minlength=num_nodes)
  
      if add_self_loop_edges:
          self_loop_edge_type_idx = PROGRAM_GRAPH_EDGES_TYPES_VOCAB[SELF_LOOP_EDGE_NAME]
          adjacency_lists[self_loop_edge_type_idx] = \
              np.stack([np.arange(num_nodes), np.arange(num_nodes)], axis=1)
          num_incoming_edges_per_type[self_loop_edge_type_idx, :] = \
              np.ones(shape=(num_nodes,))
  
      # VarMisuse-specific things: Reorder symbol candidates so that correct one is first.
      correct_candidate_id = None
      distractor_candidate_ids = []  # type: List[int]
      for candidate in raw_sample['SymbolCandidates']:
          if candidate['IsCorrect']:
              correct_candidate_id = candidate['SymbolDummyNode']
          else:
              distractor_candidate_ids.append(candidate['SymbolDummyNode'])
      assert correct_candidate_id is not None
      candidate_node_ids = [correct_candidate_id] + distractor_candidate_ids[:max_variable_candidates - 1]
      # Pad symbol candidates up to max_variable_candidates:
      num_scope_padding = max_variable_candidates - len(candidate_node_ids)
      candidate_node_ids_mask = [True] * len(candidate_node_ids) + [False] * num_scope_padding
      candidate_node_ids = candidate_node_ids + [0] * num_scope_padding
  
      return GraphSample(adjacency_lists=adjacency_lists,
                         type_to_node_to_num_incoming_edges=num_incoming_edges_per_type,
                         unique_labels_as_characters=node_label_chars_unique,
                         node_labels_to_unique_labels=node_label_chars_indices,
                         slot_node_id=raw_sample['SlotDummyNode'],
                         variable_candidate_nodes=np.array(candidate_node_ids),
                         variable_candidate_nodes_mask=np.array(candidate_node_ids_mask),
                         )
  
  
  def _data_loading_worker(path_queue: Queue,
                           result_queue: Queue,
                           unsplittable_node_names: Set[str],
                           graph_node_label_max_num_chars: int,
                           max_variable_candidates: int,
                           add_self_loop_edges: bool,
                           ) -> None:
      while True:
          next_path = path_queue.get()
          if next_path is None:  # Our signal that all files have been processed
              path_queue.put(None)  # Signal to the other workers
              result_queue.put(None)  # Signal to the controller that we are done
              break
  
          # Read the file and push examples out as soon as we get them:
          for raw_sample in next_path.read_by_file_suffix():
              result_queue.put(_load_single_sample(raw_sample,
                                                   unsplittable_node_names,
                                                   graph_node_label_max_num_chars,
                                                   max_variable_candidates,
                                                   add_self_loop_edges,
                                                   ))
  
  
  def _load_data(paths: List[RichPath],
                 unsplittable_node_names: Set[str],
                 graph_node_label_max_num_chars: int,
                 max_variable_candidates: int,
                 add_self_loop_edges: bool,
                 no_parallel: bool = False,
                 ) -> Iterable[GraphSample]:
      if no_parallel:
          return [_load_single_sample(raw_sample, unsplittable_node_names, graph_node_label_max_num_chars,
                                      max_variable_candidates, add_self_loop_edges)
                  for path in paths
                  for raw_sample in path.read_by_file_suffix()]
  
      path_queue = Queue(maxsize=len(paths) + 1)
      result_queue = Queue()
  
      # Set up list of work to do:
      for path in paths:
          path_queue.put(path)
      path_queue.put(None)  # Signal for the end of the queue
  
      # Set up workers:
      workers = []
      for _ in range(cpu_count()):
          workers.append(Process(target=_data_loading_worker,
                                 args=(path_queue,
                                       result_queue,
                                       unsplittable_node_names,
                                       graph_node_label_max_num_chars,
                                       max_variable_candidates,
                                       add_self_loop_edges,
                                       )))
          workers[-1].start()
  
      # Consume the data:
      num_workers_terminated = 0
      while num_workers_terminated < len(workers):
          parsed_sample = result_queue.get()
          if parsed_sample is None:
              num_workers_terminated += 1  # Worker signaled that it's done
          else:
              yield parsed_sample
  
      # Clean up the workers:
      for worker in workers:
          worker.join()
  
  
  class VarMisuse_Task(Sparse_Graph_Task):
      @classmethod
      def default_params(cls):
          params = super().default_params()
          params.update({
              'max_variable_candidates': 5,
              'graph_node_label_max_num_chars': 19,
              'graph_node_label_representation_size': 64,
              'slot_score_via_linear_layer': True,
              'loss_function': 'max-likelihood',  # max-likelihood or max-margin
              'max-margin_loss_margin': 0.2,
              'out_layer_dropout_rate': 0.2,
              'add_self_loop_edges': False,
              # 'max_num_data_files': 3,
          })
          return params
  
      @staticmethod
      def name() -> str:
          return "VarMisuse"
  
      @staticmethod
      def default_data_path() -> str:
          return "data/varmisuse"
  
      def __init__(self, params: Dict[str, Any]):
          super().__init__(params)
  
          # If required, add the self-loop edge type to the vocab:
          if params.get('add_self_loop_edges'):
              if SELF_LOOP_EDGE_NAME not in PROGRAM_GRAPH_EDGES_TYPES_VOCAB:
                  PROGRAM_GRAPH_EDGES_TYPES_VOCAB[SELF_LOOP_EDGE_NAME] = \
                      len(PROGRAM_GRAPH_EDGES_TYPES_VOCAB)
  
      def get_metadata(self) -> Dict[str, Any]:
          metadata = super().get_metadata()
          return metadata
  
      def restore_from_metadata(self, metadata: Dict[str, Any]) -> None:
          super().restore_from_metadata(metadata)
  
      @property
      def num_edge_types(self) -> int:
          return len(PROGRAM_GRAPH_EDGES_TYPES_VOCAB)
  
      @property
      def initial_node_feature_size(self) -> int:
          return self.params['graph_node_label_representation_size']
  
      # -------------------- Data Loading --------------------
      def load_data(self, path: RichPath) -> None:
          # Note that as __load_data produces a generator, we explicitly force loading
          # (and caching) here:
          self._loaded_data[DataFold.TRAIN] = \
              list(self.__load_data(path.join("graphs-train"), DataFold.TRAIN))
          self._loaded_data[DataFold.VALIDATION] = \
              list(self.__load_data(path.join("graphs-valid"), DataFold.VALIDATION))
  
      def load_eval_data_from_path(self, path: RichPath) -> Iterable[Any]:
          if path.path == self.default_data_path():
              path = path.join("graphs-test")
          return iter(self.__load_data(path, DataFold.TEST))
  
      def __load_data(self, data_dir: RichPath, data_fold: DataFold) -> Iterator[GraphSample]:
          all_data_files = data_dir.iterate_filtered_files_in_dir("*.gz")
  
          max_num_files = self.params.get('max_num_data_files', None)
          if max_num_files is not None:
              all_data_files = sorted(all_data_files)[:max_num_files]
          else:
              all_data_files = list(all_data_files)
          print(" Loading VarMisuse data from %s [%i data files]." % (data_dir, len(all_data_files)))
  
          unsplittable_keywords = get_language_keywords('csharp')
          return _load_data(all_data_files,
                            unsplittable_keywords,
                            self.params['graph_node_label_max_num_chars'],
                            self.params['max_variable_candidates'],
                            self.params['add_self_loop_edges'])
  
      # -------------------- Model Construction --------------------
      def make_task_input_model(self,
                                placeholders: Dict[str, tf.Tensor],
                                model_ops: Dict[str, tf.Tensor],
                                ) -> None:
          node_label_char_length = self.params['graph_node_label_max_num_chars']
          placeholders['unique_labels_as_characters'] = \
              tf.placeholder(dtype=tf.int32, shape=[None, node_label_char_length], name='unique_labels_as_characters')
          placeholders['node_labels_to_unique_labels'] = \
              tf.placeholder(dtype=tf.int32, shape=[None], name='node_labels_to_unique_labels')
          placeholders['adjacency_lists'] = \
              [tf.placeholder(dtype=tf.int32, shape=[None, 2], name='adjacency_e%s' % e)
                  for e in range(self.num_edge_types)]
          placeholders['type_to_num_incoming_edges'] = \
              tf.placeholder(dtype=tf.float32, shape=[self.num_edge_types, None], name='type_to_num_incoming_edges')
  
          model_ops['initial_node_features'] = \
              self.__get_node_label_charcnn_embeddings(placeholders['unique_labels_as_characters'],
                                                       placeholders['node_labels_to_unique_labels'])
          model_ops['adjacency_lists'] = placeholders['adjacency_lists']
          model_ops['type_to_num_incoming_edges'] = placeholders['type_to_num_incoming_edges']
  
      def __get_node_label_charcnn_embeddings(self,
                                              unique_labels_as_characters: tf.Tensor,
                                              node_labels_to_unique_labels: tf.Tensor,
                                              ) -> tf.Tensor:
          """
          Compute representation of node labels using a 2-layer character CNN.
  
          Args:
              unique_labels_as_characters: int32 tensor of shape [U, C]
                  representing the unique (node) labels occurring in a
                  batch, where U is the number of such labels and C the
                  maximal number of characters.
              node_labels_to_unique_labels: int32 tensor of shape [V],
                  mapping each node in the batch to one of the unique
                  labels.
  
          Returns:
              float32 tensor of shape [V, D] representing embedded node
              label information about each node.
          """
          label_embedding_size = self.params['graph_node_label_representation_size']  # D
          # U ~ num unique labels
          # C ~ num characters (self.params['graph_node_label_max_num_chars'])
          # A ~ num characters in alphabet
          unique_label_chars_one_hot = tf.one_hot(indices=unique_labels_as_characters,
                                                  depth=len(ALPHABET),
                                                  axis=-1)  # Shape: [U, C, A]
  
          # Choose kernel sizes such that there is a single value at the end:
          char_conv_l1_kernel_size = 5
          char_conv_l2_kernel_size = \
              self.params['graph_node_label_max_num_chars'] - 2 * (char_conv_l1_kernel_size - 1)
  
          char_conv_l1 = \
              tf.keras.layers.Conv1D(filters=16,
                                     kernel_size=char_conv_l1_kernel_size,
                                     activation=tf.nn.leaky_relu,
                                     )(unique_label_chars_one_hot)  # Shape: [U, C - (char_conv_l1_kernel_size - 1), 16]
          char_pool_l1 = \
              tf.keras.layers.MaxPool1D(pool_size=char_conv_l1_kernel_size,
                                        strides=1,
                                        )(inputs=char_conv_l1)      # Shape: [U, C - 2*(char_conv_l1_kernel_size - 1), 16]
          char_conv_l2 = \
              tf.keras.layers.Conv1D(filters=label_embedding_size,
                                     kernel_size=char_conv_l2_kernel_size,
                                     activation=tf.nn.leaky_relu,
                                     )(char_pool_l1)                # Shape: [U, 1, D]
          unique_label_representations = tf.squeeze(char_conv_l2, axis=1)  # Shape: [U, D]
          node_label_representations = tf.gather(params=unique_label_representations,
                                                 indices=node_labels_to_unique_labels)
          return node_label_representations
  
      def make_task_output_model(self,
                                 placeholders: Dict[str, tf.Tensor],
                                 model_ops: Dict[str, tf.Tensor],
                                 ) -> None:
          placeholders['slot_node_ids'] = \
              tf.placeholder(dtype=tf.int32, shape=[None], name='slot_node_ids')
          placeholders['candidate_node_ids'] = \
              tf.placeholder(dtype=tf.int32, shape=[None, None], name='candidate_node_ids')
          placeholders['candidate_node_ids_mask'] = \
              tf.placeholder(dtype=tf.float32, shape=[None, None], name='candidate_node_ids_mask')
          placeholders['out_layer_dropout_rate'] = \
              tf.placeholder_with_default(0.0, shape=[], name='out_layer_dropout_rate')
  
          final_node_repr_size = model_ops['final_node_representations'].shape.as_list()[-1]
          num_candidate_vars = self.params['max_variable_candidates']
  
          final_node_states = \
              tf.nn.dropout(model_ops['final_node_representations'],
                            keep_prob=placeholders['out_layer_dropout_rate'])  # Shape: [V, D]
  
          # --- (1) Collect representation of slots and candidates:
          slot_representations = \
              tf.gather(params=final_node_states, indices=placeholders['slot_node_ids'])  # Shape: [G, D]
          # Make things fit into 1D gather:
          candidate_node_ids = tf.reshape(placeholders['candidate_node_ids'], shape=[-1])
          candidate_representations = \
              tf.gather(params=final_node_states, indices=candidate_node_ids)  # Shape: [G * Cands, D]
          candidate_representations = \
              tf.reshape(candidate_representations,
                         shape=[-1, num_candidate_vars, final_node_repr_size])  # Shape: [G, Cands, D]
  
          # --- (2) Compute match between final candidate representations and slot representation:
          slot_candidate_inner_product = \
              tf.einsum('sd,scd->sc', slot_representations, candidate_representations)  # Shape: [G, Cands]
  
          if self.params['slot_score_via_linear_layer']:
              repeated_slots = tf.tile(tf.expand_dims(slot_representations, axis=1),
                                       multiples=[1, num_candidate_vars, 1])  # Shape: [G, Cands, D]
              slot_cand_comb = tf.concat([candidate_representations,
                                          repeated_slots,
                                          tf.expand_dims(slot_candidate_inner_product, -1)],
                                         axis=2)  # Shape: [G, Cands, 2*D + 1]
              logits = tf.keras.layers.Dense(units=1,
                                             use_bias=False,
                                             activation=None,
                                             name='slot_score_linear_layer'
                                             )(slot_cand_comb)  # Shape: [G, Cands, 1]
              logits = tf.squeeze(logits, axis=-1)  # Shape: [G, Cands]
          else:
              logits = slot_candidate_inner_product
  
          logits += (1.0 - placeholders['candidate_node_ids_mask']) * -BIG_NUMBER
  
          # --- (3) Compute loss & metrics:
          loss_function = self.params['loss_function']
          # Note that by convention, the first candidate is always the correct one:
          correct_choices = tf.zeros([tf.shape(logits)[0]], dtype=tf.int32)
          if loss_function == 'max-likelihood':
              per_graph_loss = \
                  tf.nn.sparse_softmax_cross_entropy_with_logits(labels=correct_choices, logits=logits)
          elif loss_function == 'max-margin':
              log_probs = tf.nn.log_softmax(logits)
              correct_log_prob = log_probs[:, 0]
              max_wrong_log_prob = tf.reduce_max(log_probs[:, 1:], axis=1)
              per_graph_loss = \
                  tf.nn.relu(max_wrong_log_prob - correct_log_prob + self.parameters['loss_margin'])
          else:
              raise Exception('Invalid loss function option: "%s"' % loss_function)
  
          prediction_is_correct = tf.equal(tf.argmax(tf.nn.softmax(logits), 1, output_type=tf.int32),
                                           correct_choices)
          accuracy = tf.reduce_mean(tf.cast(prediction_is_correct, tf.float32))
  
          tf.summary.scalar('accuracy', accuracy)
          model_ops['task_metrics'] = {
              'loss': tf.reduce_mean(per_graph_loss),
              'total_loss': tf.reduce_sum(per_graph_loss),
              'accuracy': accuracy,
              'num_correct_predictions': tf.reduce_sum(tf.cast(prediction_is_correct, tf.int32)),
          }
  
      # -------------------- Minibatching and training loop --------------------
      def make_minibatch_iterator(self,
                                  data: Iterable[Any],
                                  data_fold: DataFold,
                                  model_placeholders: Dict[str, tf.Tensor],
                                  max_nodes_per_batch: int) \
              -> Iterable[MinibatchData]:
          if data_fold == DataFold.TRAIN:
              np.random.shuffle(data)
  
          if isinstance(data, Iterator):
              data_iter = data
          else:
              data_iter = iter(data)
  
          def init_raw_batch_data_holder() -> Dict[str, Any]:
              return {
                  'adj_lists': [[] for _ in range(self.num_edge_types)],
                  'type_to_num_in_edges': [],
                  'uniq_labels_as_chars': [],
                  'node_labels_to_uniq_labels': [],
                  'slot_node_ids': [],
                  'candidate_node_ids': [],
                  'candidate_node_ids_mask': [],
                  'num_graphs': 0,
                  'node_offset': 0,
                  'unique_label_offset': 0,
              }
  
          def finalise_batch_data(raw_batch_data: Dict[str, Any]) -> MinibatchData:
              batch_feed_dict = {
                  model_placeholders['unique_labels_as_characters']: np.concatenate(raw_batch_data['uniq_labels_as_chars'], axis=0),
                  model_placeholders['node_labels_to_unique_labels']: np.concatenate(raw_batch_data['node_labels_to_uniq_labels'], axis=0),
                  model_placeholders['type_to_num_incoming_edges']: np.concatenate(raw_batch_data['type_to_num_in_edges'], axis=1),
                  model_placeholders['slot_node_ids']: raw_batch_data['slot_node_ids'],
                  model_placeholders['candidate_node_ids']: raw_batch_data['candidate_node_ids'],
                  model_placeholders['candidate_node_ids_mask']: raw_batch_data['candidate_node_ids_mask'],
              }
  
              if data_fold == DataFold.TRAIN:
                  model_placeholders['out_layer_dropout_rate'] = self.params['out_layer_dropout_rate']
  
              # Merge adjacency lists:
              num_edges = 0
              for i in range(self.num_edge_types):
                  if len(raw_batch_data['adj_lists'][i]) > 0:
                      adj_list = np.concatenate(raw_batch_data['adj_lists'][i])
                  else:
                      adj_list = np.zeros((0, 2), dtype=np.int32)
                  num_edges += adj_list.shape[0]
                  batch_feed_dict[model_placeholders['adjacency_lists'][i]] = adj_list
  
              return MinibatchData(feed_dict=batch_feed_dict,
                                   num_graphs=raw_batch_data['num_graphs'],
                                   num_nodes=raw_batch_data['node_offset'],
                                   num_edges=num_edges)
  
          try:
              cur_batch_data = init_raw_batch_data_holder()
              while True:
                  cur_graph = next(data_iter)
                  # We pack until we cannot fit more graphs in the batch, yield, and continue:
                  if cur_batch_data['node_offset'] + len(cur_graph.node_labels_to_unique_labels) >= max_nodes_per_batch:
                      yield finalise_batch_data(cur_batch_data)
                      cur_batch_data = init_raw_batch_data_holder()
  
                  # Graph structure:
                  for i in range(self.num_edge_types):
                      cur_batch_data['adj_lists'][i].append(cur_graph.adjacency_lists[i] + cur_batch_data['node_offset'])
                  cur_batch_data['type_to_num_in_edges'].append(cur_graph.type_to_node_to_num_incoming_edges)
  
                  # Node labels:
                  cur_batch_data['uniq_labels_as_chars'].append(cur_graph.unique_labels_as_characters)
                  cur_batch_data['node_labels_to_uniq_labels'].append(
                      cur_graph.node_labels_to_unique_labels + cur_batch_data['unique_label_offset'])
                  cur_batch_data['unique_label_offset'] += cur_graph.unique_labels_as_characters.shape[0]
  
                  # VarMisuse task bits:
                  cur_batch_data['slot_node_ids'].append(cur_graph.slot_node_id + cur_batch_data['node_offset'])
                  cur_batch_data['candidate_node_ids'].append(cur_graph.variable_candidate_nodes + cur_batch_data['node_offset'])
                  cur_batch_data['candidate_node_ids_mask'].append(cur_graph.variable_candidate_nodes_mask)
  
                  # Finally, update the offset we use to shift things during batch construction:
                  cur_batch_data['num_graphs'] += 1
                  cur_batch_data['node_offset'] += len(cur_graph.node_labels_to_unique_labels)
          except StopIteration:
              # Final batch, yield only if non-empty:
              if cur_batch_data['num_graphs'] > 0:
                  yield finalise_batch_data(cur_batch_data)
  
      def early_stopping_metric(self, task_metric_results: List[Dict[str, np.ndarray]], num_graphs: int) -> float:
          # Early stopping based on accuracy; as we are trying to minimize, negate it:
          acc = sum([m['num_correct_predictions'] for m in task_metric_results]) / float(num_graphs)
          return -acc
  
      def pretty_print_epoch_task_metrics(self, task_metric_results: List[Dict[str, np.ndarray]], num_graphs: int) -> str:
          acc = sum([m['num_correct_predictions'] for m in task_metric_results]) / float(num_graphs)
          return "Accuracy: %.3f" % (acc,)
  
apis: numpy.ones tensorflow.summary.scalar tensorflow.reduce_max tensorflow.reshape tensorflow.squeeze tensorflow.einsum tensorflow.nn.sparse_softmax_cross_entropy_with_logits tensorflow.nn.softmax tensorflow.reduce_sum tensorflow.nn.dropout tensorflow.keras.layers.Conv1D numpy.concatenate tensorflow.nn.log_softmax tensorflow.keras.layers.Dense numpy.unique tensorflow.nn.relu numpy.bincount tensorflow.shape numpy.zeros tensorflow.expand_dims numpy.arange tensorflow.cast tensorflow.placeholder_with_default tensorflow.placeholder numpy.random.shuffle tensorflow.reduce_mean numpy.flip numpy.array tensorflow.gather tensorflow.keras.layers.MaxPool1D