code: def fit(self, x, num_workers=None, **fit_params):
      from bigdl.nano.utils.common import invalidInputError
  
      if num_workers:
          invalidInputError(
              False,
              "We don't support passing num_workers in fit "
              "with input of xShards of builtins.dict",
          )
  
      def orca_train_model(d, config):
          tcmf = TCMF()
          tcmf.build(config)
          id_arr, train_data = split_id_and_data(d, True)
          tcmf.fit_eval((train_data, None), **fit_params)
          return [id_arr, tcmf]
  
      if isinstance(x, SparkXShards):
          if x._get_class_name() == "builtins.dict":
              self.internal = x.transform_shard(
                  orca_train_model, self.config
              )
          else:
              invalidInputError(
                  False,
                  "value of x should be an xShards of builtins.dict, "
                  "but is an xShards of " + x._get_class_name(),
              )
      else:
          invalidInputError(
              False,
              "value of x should be an xShards of builtins.dict, "
              "but isn't an xShards",
          )
  
imports: from abc import ABCMeta, abstractmethod
  from bigdl.chronos.model.tcmf import DeepGLO
  from bigdl.orca.automl.metrics import Evaluator
  from bigdl.orca.automl.model.abstract import BaseModel
  from bigdl.orca.data import SparkXShards, XShards
  import pickle
  import numpy
  import pandas
label_desc: Use image node_id 2 for calling the TCMFXshardsModelWrapper obj's underlying member method code with example usage: obj.fit(x, num_workers) and returns: list