class_docstr: Create a PyTorch dataset of a tabularized time-series
class_docstr_tok: ["Create","a","PyTorch","dataset","of","a","tabularized","time-series"]
docstr: Initialize Timedataset from time-series df.
  Parameters
  ----------
      df : pd.DataFrame
          Time series data
      name : str
          Name of time-series
      **kwargs : dict
          Identical to :meth:`tabularize_univariate_datetime`
docstr_tok: ["Initialize","Timedataset","from","time-series","df",".","Parameters","--","--","--","--","--","df",":","pd.DataFrame","Time","series","data","name",":","str","Name","of","time-series","*","*","kwargs",":","dict","Identical","to",":","meth",":","`","tabularize_univariate_datetime","`"]
code: def __init__(self, df, name, **kwargs):
      """Initialize Timedataset from time-series df.
      Parameters
      ----------
          df : pd.DataFrame
              Time series data
          name : str
              Name of time-series
          **kwargs : dict
              Identical to :meth:`tabularize_univariate_datetime`
      """
      self.name = name
      self.length = None
      self.inputs = OrderedDict({})
      self.targets = None
      self.meta = OrderedDict({})
      self.two_level_inputs = [
          "seasonalities",
          "covariates",
          "events",
          "regressors",
      ]
      inputs, targets, drop_missing = tabularize_univariate_datetime(
          df, **kwargs
      )
      self.init_after_tabularized(inputs, targets)
      self.filter_samples_after_init(kwargs["prediction_frequency"])
      self.drop_nan_after_init(
          df, kwargs["predict_steps"], drop_missing
      )
  
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 1 to create a new TimeDataset object from inherited base classes: Dataset with example: obj = TimeDataset(df, name)