docstr: Collate a list of dictionaries into one.
  Each value is a tensor concatenated from a list of batch tensors.
  
  Parameters
  ----------
  x
      A list of batch results. Each batch is one (nested) dictionary.
docstr_tok: ["Collate","a","list","of","dictionaries","into","one",".","Each","value","is","a","tensor","concatenated","from","a","list","of","batch","tensors",".","Parameters","--","--","--","--","--","x","A","list","of","batch","results",".","Each","batch","is","one","(","nested",")","dictionary","."]
code: def collate(self, x: List[Dict]):
      """
      Collate a list of dictionaries into one.
      Each value is a tensor concatenated from a list of batch tensors.
  
      Parameters
      ----------
      x
          A list of batch results. Each batch is one (nested) dictionary.
      """
      if BBOX in x[0]:  # for detection outputs just sort the batches
          return x
  
      results = dict()
      if len(x[0]) == 0:  # dict is empty
          return dict()
  
      for k, v in x[0].items():
          if k in [WEIGHT, LOGIT_SCALE]:  # ignore the keys
              continue
          elif isinstance(v, dict):
              results[k] = self.collate([i[k] for i in x])
          elif isinstance(v, torch.Tensor):
              results[k] = torch.cat([i[k] for i in x])
          else:
              raise ValueError(
                  f"Unsupported data type {type(v)} is found in prediction results. "
                  f"We only support dictionary with torch tensor values."
              )
  
      return results
  
imports: import logging
  import os
  import shutil
  import time
  import uuid
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Sequence
  import lightning.pytorch
  import torch
  from lightning.pytorch.callbacks import BasePredictionWriter
  from ..constants import BBOX, LM_TARGET, LOGIT_SCALE, LOGITS, TEMPLATE_LOGITS, WEIGHT
label_desc: Use image node_id 7 for calling the DDPPredictionWriter obj's underlying member method code with example usage: obj.collate(x) and returns: results, x, dict