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 validate_train_spec(self):
      """Placeholder docstring"""
      train_spec = self.algorithm_spec["TrainingSpecification"]
      algorithm_name = self.algorithm_spec["AlgorithmName"]
  
      # Check that the input mode provided is compatible with the training input modes for the
      # algorithm.
      input_modes = self._algorithm_training_input_modes(
          train_spec["TrainingChannels"]
      )
      if (
          not is_pipeline_variable(self.input_mode)
          and self.input_mode not in input_modes
      ):
          raise ValueError(
              "Invalid input mode: %s. %s only supports: %s"
              % (self.input_mode, algorithm_name, input_modes)
          )
  
      # Check that the training instance type is compatible with the algorithm.
      supported_instances = train_spec["SupportedTrainingInstanceTypes"]
      if (
          not is_pipeline_variable(self.instance_type)
          and self.instance_type not in supported_instances
      ):
          raise ValueError(
              "Invalid instance_type: %s. %s supports the following instance types: %s"
              % (
                  self.instance_type,
                  algorithm_name,
                  supported_instances,
              )
          )
  
      # Verify if distributed training is supported by the algorithm
      if not is_pipeline_variable(self.instance_count) and (
          self.instance_count > 1
          and "SupportsDistributedTraining" in train_spec
          and not train_spec["SupportsDistributedTraining"]
      ):
          raise ValueError(
              "Distributed training is not supported by %s. "
              "Please set instance_count=1" % algorithm_name
          )
  
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 2 for calling the AlgorithmEstimator obj's underlying member method code with example usage: obj.validate_train_spec() without return types