class_docstr: Strategy for applying custom regression models from FEDOT make predictions
class_docstr_tok: ["Strategy","for","applying","custom","regression","models","from","FEDOT","make","predictions"]
docstr: This method is used for operation training 
docstr_tok: ["This","method","is","used","for","operation","training"]
code: def fit(self, train_data: InputData):
      """This method is used for operation training"""
  
      warnings.filterwarnings("ignore", category=RuntimeWarning)
      operation_implementation = self.operation_impl(
          self.params_for_fit
      )
      with ImplementationRandomStateHandler(
          implementation=operation_implementation
      ):
          operation_implementation.fit(train_data)
      return operation_implementation
  
imports: import warnings
  from typing import Optional
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.evaluation_interfaces import EvaluationStrategy, SkLearnEvaluationStrategy
  from fedot.core.operations.evaluation.operation_implementations.data_operations.decompose import DecomposerRegImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_filters import IsolationForestRegImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_filters import LinearRegRANSACImplementation, NonLinearRegRANSACImplementation
  from fedot.core.operations.evaluation.operation_implementations.data_operations.sklearn_selectors import LinearRegFSImplementation, NonLinearRegFSImplementation
  from fedot.core.operations.evaluation.operation_implementations.models.knn import FedotKnnRegImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.utilities.random import ImplementationRandomStateHandler
label_desc: Use image node_id 2 for calling the FedotRegressionStrategy obj's underlying member method code with example usage: obj.fit(train_data) and returns: operation_implementation