docstr: Method for creating torch.utils.data.DataLoader object from input_data
  
  Generate lag tables and process it into DataLoader
  
  :param input_data: data with features, target and ids to process
  :return torch.utils.data.DataLoader: DataLoader with train data
docstr_tok: ["Method","for","creating","torch.utils.data.DataLoader","object","from","input_data","Generate","lag","tables","and","process","it","into","DataLoader",":","param","input_data",":","data","with","features",",","target","and","ids","to","process",":","return","torch.utils.data.DataLoader",":","DataLoader","with","train","data"]
code: def _create_dataloader(self, input_data: InputData):
      """Method for creating torch.utils.data.DataLoader object from input_data
  
      Generate lag tables and process it into DataLoader
  
      :param input_data: data with features, target and ids to process
      :return torch.utils.data.DataLoader: DataLoader with train data
      """
      x, y = self._fit_transform_scaler(input_data)
      x, y = (
          torch.from_numpy(np_data.astype(np.float32))
          for np_data in (x, y)
      )
      return DataLoader(
          TensorDataset(x, y), batch_size=self.params.get("batch_size")
      )
  
imports: import numpy
  from golem.utilities.requirements_notificator import warn_requirement
  from fedot.core.data.data import InputData
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import ModelImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.dataset_types import DataTypesEnum
label_desc: Use image node_id 9 for calling the CGRUImplementation obj's underlying member method code with example usage: obj._create_dataloader(input_data) and returns: DataLoader