docstr: incrementally fit the model. Note that we only incrementally fit X_seq (TCN in global model)
  :param x_incr: 2-D numpy array in shape (n, T_incr), where n is the number of target time
  series, T_incr is the number of time steps incremented.
      incremental data to be fitted.
  :param covariates_incr: covariates corresponding to x_incr. 2-D ndarray or None.
      The shape of ndarray should be (r, T_incr), where r is the number of covariates.
      Global covariates for all time series. If None, only default time coveriates will be
      used while use_time is True. If not, the time coveriates used is the stack of input
      covariates and default time coveriates.
  :param dti_incr: dti corresponding to the x_incr. DatetimeIndex or None.
      If None, use default fixed frequency DatetimeIndex generated with the last date of x in
      fit and freq.
  :return:
docstr_tok: ["incrementally","fit","the","model",".","Note","that","we","only","incrementally","fit","X_seq","(","TCN","in","global","model",")",":","param","x_incr",":","2-D","numpy","array","in","shape","(","n",",","T_incr",")",",","where","n","is","the","number","of","target","time","series",",","T_incr","is","the","number","of","time","steps","incremented",".","incremental","data","to","be","fitted",".",":","param","covariates_incr",":","covariates","corresponding","to","x_incr",".","2-D","ndarray","or","None",".","The","shape","of","ndarray","should","be","(","r",",","T_incr",")",",","where","r","is","the","number","of","covariates",".","Global","covariates","for","all","time","series",".","If","None",",","only","default","time","coveriates","will","be","used","while","use_time","is","True",".","If","not",",","the","time","coveriates","used","is","the","stack","of","input","covariates","and","default","time","coveriates",".",":","param","dti_incr",":","dti","corresponding","to","the","x_incr",".","DatetimeIndex","or","None",".","If","None",",","use","default","fixed","frequency","DatetimeIndex","generated","with","the","last","date","of","x","in","fit","and","freq",".",":","return",":"]
code: def fit_incremental(
      self, x_incr, covariates_incr=None, dti_incr=None
  ):
      """
      incrementally fit the model. Note that we only incrementally fit X_seq (TCN in global model)
      :param x_incr: 2-D numpy array in shape (n, T_incr), where n is the number of target time
      series, T_incr is the number of time steps incremented.
          incremental data to be fitted.
      :param covariates_incr: covariates corresponding to x_incr. 2-D ndarray or None.
          The shape of ndarray should be (r, T_incr), where r is the number of covariates.
          Global covariates for all time series. If None, only default time coveriates will be
          used while use_time is True. If not, the time coveriates used is the stack of input
          covariates and default time coveriates.
      :param dti_incr: dti corresponding to the x_incr. DatetimeIndex or None.
          If None, use default fixed frequency DatetimeIndex generated with the last date of x in
          fit and freq.
      :return:
      """
      if isinstance(x_incr, dict):
          incr_id_arr, incr_train_data = split_id_and_data(
              x_incr, False
          )
          incr_train_data = self._rearrange_data_by_id(
              id_new=incr_id_arr,
              data_new=incr_train_data,
              method_name="fit_incremental",
          )
          self.internal.fit_incremental(
              incr_train_data,
              covariates_new=covariates_incr,
              dti_new=dti_incr,
          )
      else:
          raise ValueError("value of x should be a dict of ndarray")
  
imports: from abc import ABCMeta, abstractmethod
  from zoo.chronos.model.tcmf import DeepGLO
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel
  from zoo.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 4 for calling the TCMFNdarrayModelWrapper obj's underlying member method code with example usage: obj.fit_incremental(x_incr, covariates_incr, dti_incr) without return types