class_docstr: The class defines the interface of nodes modifying tha data flow in the :class:`Pipeline`
  
  Args:
      operation_type: operation defined in the operation repository
      nodes_from: parent nodes where data comes from
      node_data: ``dict`` with :class:`InputData` for fit and predict stage
      kwargs: optional arguments (i.e. logger)
docstr: Runs prediction process in the node
  
  Args:
      input_data: data used for prediction
      output_mode: desired output for operations (e.g. ``'labels'``, ``'probs'``, ``'full_probs'``)
  
  Returns:
      OutputData: values predicted on the provided ``input_data``
docstr_tok: ["Runs","prediction","process","in","the","node","Args",":","input_data",":","data","used","for","prediction","output_mode",":","desired","output","for","operations","(","e.g",".","``","'labels","'","``",",","``","'probs","'","``",",","``","'full_probs","'","``",")","Returns",":","OutputData",":","values","predicted","on","the","provided","``","input_data","``"]
code: def predict(
      self, input_data: InputData, output_mode: str = "default"
  ) -> OutputData:
      """Runs prediction process in the node
  
      Args:
          input_data: data used for prediction
          output_mode: desired output for operations (e.g. ``'labels'``, ``'probs'``, ``'full_probs'``)
  
      Returns:
          OutputData: values predicted on the provided ``input_data``
      """
      self.log.debug(
          f"Obtain prediction in pipeline node by operation: {self.operation}"
      )
  
      input_data = self._get_input_data(
          input_data=input_data, parent_operation="predict"
      )
  
      with Timer() as t:
          operation_predict = self.operation.predict(
              fitted_operation=self.fitted_operation,
              params=self._parameters,
              data=input_data,
              output_mode=output_mode,
          )
          self.inference_time_in_seconds = round(
              t.seconds_from_start, 3
          )
      return operation_predict
  
imports: from __future__ import annotations
  from dataclasses import dataclass
  from typing import Any, List, Optional, Tuple, Union
  import numpy
  from golem.core.dag.linked_graph_node import LinkedGraphNode
  from golem.core.log import default_log
  from golem.core.optimisers.timer import Timer
  from golem.serializers.serializer import register_serializable
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.data.merge.data_merger import DataMerger
  from fedot.core.operations.factory import OperationFactory
  from fedot.core.operations.operation import Operation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from fedot.core.utils import DEFAULT_PARAMS_STUB, NESTED_PARAMS_LABEL
label_desc: Use image node_id 13 for calling the PipelineNode obj's underlying member method code with example usage: obj.predict(input_data, output_mode) and returns: operation_predict