class_docstr: A generic Estimator to train using any algorithm object (with an ``algorithm_arn``).
  
  The Algorithm can be your own, or any Algorithm from AWS
  Marketplace that you have a valid subscription for. This class will perform
  client-side validation on all the inputs.
docstr: Placeholder docstring
code: def _algorithm_training_input_modes(self, training_channels):
      """Placeholder docstring"""
      current_input_modes = {"File", "Pipe"}
      for channel in training_channels:
          supported_input_modes = set(channel["SupportedInputModes"])
          current_input_modes = (
              current_input_modes & supported_input_modes
          )
  
      return current_input_modes
  
imports: from __future__ import absolute_import
  from typing import Optional, Union, Dict, List
  import sagemaker
  import sagemaker.parameter
  from sagemaker import vpc_utils
  from sagemaker.deserializers import BytesDeserializer
  from sagemaker.deprecations import removed_kwargs
  from sagemaker.estimator import EstimatorBase
  from sagemaker.inputs import TrainingInput, FileSystemInput
  from sagemaker.serializers import IdentitySerializer
  from sagemaker.transformer import Transformer
  from sagemaker.predictor import Predictor
  from sagemaker.session import Session
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.utils import format_tags, Tags
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 18 for calling the AlgorithmEstimator obj's underlying member method code with example usage: obj._algorithm_training_input_modes(training_channels) and returns: current_input_modes