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 finalize_minibatch(
      self,
      accumulated_minibatch_data: Dict[str, Any],
      device: Union[str, torch.device],
  ) -> Dict[str, Any]:
      max_seq_length = max(
          accumulated_minibatch_data["target_seq_lengths"]
      )
      num_elements = len(accumulated_minibatch_data["target_token_ids"])
  
      target_token_ids = np.zeros(
          (num_elements, max_seq_length), dtype=np.int32
      )
  
      # These need to index into index into [num-inputs-flattened, max-seq-size - 1] memories
      copyable_elements_idxs: List[int] = []
      # Each group of elements is one entry of a [num-target, max-seq-size - 1]
      copyable_elements_sample_idxs: List[int] = []
  
      # Count how many input sequences have been seen up to each point of the iteration
      offset_num_input_elements_so_far = 0
      for sample_idx, (
          token_ids,
          copyable_elements,
          num_input_elements_for_sample,
      ) in enumerate(
          zip(
              accumulated_minibatch_data["target_token_ids"],
              accumulated_minibatch_data["copyable_elements_idxs"],
              accumulated_minibatch_data["num_input_elements"],
          )
      ):
          target_token_ids[sample_idx, : len(token_ids)] = token_ids
  
          sample_row_starting_idx = sample_idx * (max_seq_length - 1)
          for timestep_idx, copyable_elements_at_timestep in enumerate(
              copyable_elements
          ):
              flattened_index = (
                  sample_row_starting_idx + timestep_idx
              )  # The -1 is here since the flattened output does not have a prediction for the last element
              # Confusingly this is indexed by timestep. So (after the offset) we have a flattened out [num-inputs-flattened, max-seq-size-1]
              copyable_elements_idxs.extend(
                  offset_num_input_elements_so_far
                  + copyable_elements_at_timestep * (max_seq_length - 1)
                  + timestep_idx
              )
              copyable_elements_sample_idxs.extend(
                  flattened_index
                  for _ in range(len(copyable_elements_at_timestep))
              )
              # Add one full sequence of inputs here
          offset_num_input_elements_so_far += (
              num_input_elements_for_sample * (max_seq_length - 1)
          )
  
      return {
          "target_token_ids": torch.tensor(
              target_token_ids, dtype=torch.int64, device=device
          ),
          "copyable_elements_idxs": torch.tensor(
              copyable_elements_idxs, dtype=torch.int64, device=device
          ),
          "copyable_elements_sample_idxs": torch.tensor(
              copyable_elements_sample_idxs,
              dtype=torch.int64,
              device=device,
          ),
          "target_lengths": torch.tensor(
              accumulated_minibatch_data["target_seq_lengths"],
              dtype=torch.int64,
              device=device,
          ),
      }
  
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 11 for calling the GruCopyingDecoderModel obj's underlying member method code with example usage: obj.finalize_minibatch(accumulated_minibatch_data, device) and returns: dict