class_docstr: Create a PyTorch dataset of a tabularized time-series
class_docstr_tok: ["Create","a","PyTorch","dataset","of","a","tabularized","time-series"]
docstr: Split nested dict into list of dicts.
  Parameters
  ----------
      inputs : ordered dict
          Nested dict to be split.
  Returns
  -------
      list of dicts
          List of dicts with same keys as inputs.
docstr_tok: ["Split","nested","dict","into","list","of","dicts",".","Parameters","--","--","--","--","--","inputs",":","ordered","dict","Nested","dict","to","be","split",".","Returns","--","--","--","-","list","of","dicts","List","of","dicts","with","same","keys","as","inputs","."]
code: def _split_nested_dict(inputs):
      """Split nested dict into list of dicts.
      Parameters
      ----------
          inputs : ordered dict
              Nested dict to be split.
      Returns
      -------
          list of dicts
              List of dicts with same keys as inputs.
      """
  
      def split_dict(inputs, index):
          return {
              k: v[index]
              if not isinstance(v, dict)
              else split_dict(v, index)
              for k, v in inputs.items()
          }
  
      length = next(iter(inputs.values())).shape[0]
      return [split_dict(inputs, i) for i in range(length)]
  
imports: import logging
  from collections import OrderedDict, defaultdict
  from datetime import datetime
  from typing import Optional
  import numpy
  import pandas
  import torch
  from torch.utils.data.dataset import Dataset
  from neuralprophet import configure, utils
  from neuralprophet.df_utils import get_max_num_lags
  from neuralprophet.hdays_utils import get_country_holidays
label_desc: Use image node_id 3 for calling the TimeDataset obj's underlying member method code with example usage: obj._split_nested_dict(inputs) and returns: unknown, unknown