class_docstr: A GRU copying decoder that accepts an variable-sized set of encodings.
class_docstr_tok: ["A","GRU","copying","decoder","that","accepts","an","variable-sized","set","of","encodings","."]
code: def extend_minibatch_with(
      self,
      tensorized_datapoint: TokenizedOutput,
      partial_minibatch: Dict[str, Any],
  ) -> bool:
      partial_minibatch["target_token_ids"].append(
          tensorized_datapoint.token_ids
      )
      partial_minibatch["target_seq_lengths"].append(
          tensorized_datapoint.length
      )
      partial_minibatch["copyable_elements_idxs"].append(
          tensorized_datapoint.copyable_elements
      )
      partial_minibatch["num_input_elements"].append(
          tensorized_datapoint.num_input_elements
      )
      return True
  
imports: from typing_extensions import Final
  import logging
  import math
  import numpy
  import torch
  import torch.nn
  from dpu_utils.mlutils import Vocabulary
  from torch_scatter import scatter_add
  from torch_scatter.composite import scatter_log_softmax, scatter_logsumexp
  from typing import Any, Counter, Dict, List, NamedTuple, Tuple, Union
  from ptgnn.baseneuralmodel import AbstractNeuralModel
label_desc: Use image node_id 10 for calling the GruCopyingDecoderModel obj's underlying member method code with example usage: obj.extend_minibatch_with(tensorized_datapoint, partial_minibatch) and returns: True