class_docstr: A SageMakerClient that implements the API calls locally.
  
  Used for doing local training and hosting local endpoints. It still needs access to
  a boto client to interact with S3 but it won't perform any SageMaker call.
  
  Implements the methods with the same signature as the boto SageMakerClient.
  
  Args:
  
  Returns:
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docstr: Create a training job in Local Mode.
  
  Args:
    TrainingJobName(str): local training job name.
    AlgorithmSpecification(dict): Identifies the training algorithm to use.
    InputDataConfig(dict, optional): Describes the training dataset and the location where
      it is stored. (Default value = None)
    OutputDataConfig(dict): Identifies the location where you want to save the results of
      model training.
    ResourceConfig(dict): Identifies the resources to use for local model training.
    Environment(dict, optional): Describes the environment variables to pass
      to the container. (Default value = None)
    HyperParameters(dict) [optional]: Specifies these algorithm-specific parameters to
      influence the quality of the final model.
    **kwargs:
  
  Returns:
code: def create_training_job(
      self,
      TrainingJobName,
      AlgorithmSpecification,
      OutputDataConfig,
      ResourceConfig,
      InputDataConfig=None,
      Environment=None,
      **kwargs,
  ):
      """Create a training job in Local Mode.
  
      Args:
        TrainingJobName(str): local training job name.
        AlgorithmSpecification(dict): Identifies the training algorithm to use.
        InputDataConfig(dict, optional): Describes the training dataset and the location where
          it is stored. (Default value = None)
        OutputDataConfig(dict): Identifies the location where you want to save the results of
          model training.
        ResourceConfig(dict): Identifies the resources to use for local model training.
        Environment(dict, optional): Describes the environment variables to pass
          to the container. (Default value = None)
        HyperParameters(dict) [optional]: Specifies these algorithm-specific parameters to
          influence the quality of the final model.
        **kwargs:
  
      Returns:
  
      """
      InputDataConfig = InputDataConfig or {}
      Environment = Environment or {}
      container = _SageMakerContainer(
          ResourceConfig["InstanceType"],
          ResourceConfig["InstanceCount"],
          AlgorithmSpecification["TrainingImage"],
          sagemaker_session=self.sagemaker_session,
      )
      if AlgorithmSpecification.get("ContainerEntrypoint", None):
          container.container_entrypoint = AlgorithmSpecification[
              "ContainerEntrypoint"
          ]
      if AlgorithmSpecification.get("ContainerArguments", None):
          container.container_arguments = AlgorithmSpecification[
              "ContainerArguments"
          ]
      training_job = _LocalTrainingJob(container)
      hyperparameters = (
          kwargs["HyperParameters"]
          if "HyperParameters" in kwargs
          else {}
      )
      logger.info("Starting training job")
      training_job.start(
          InputDataConfig,
          OutputDataConfig,
          hyperparameters,
          Environment,
          TrainingJobName,
      )
  
      LocalSagemakerClient._training_jobs[
          TrainingJobName
      ] = training_job
  
imports: from __future__ import absolute_import, annotations
  import logging
  import platform
  from datetime import datetime
  from typing import Dict
  import boto3
  from botocore.exceptions import ClientError
  import jsonschema
  from sagemaker.config import SAGEMAKER_PYTHON_SDK_LOCAL_MODE_CONFIG_SCHEMA, SESSION_DEFAULT_S3_BUCKET_PATH, SESSION_DEFAULT_S3_OBJECT_KEY_PREFIX_PATH, load_local_mode_config, load_sagemaker_config, validate_sagemaker_config
  from sagemaker.local.image import _SageMakerContainer
  from sagemaker.local.utils import get_docker_host
  from sagemaker.local.entities import _LocalEndpointConfig, _LocalEndpoint, _LocalModel, _LocalProcessingJob, _LocalTrainingJob, _LocalTransformJob, _LocalPipeline
  from sagemaker.session import Session
  from sagemaker.utils import get_config_value, _module_import_error, resolve_value_from_config, format_tags
label_desc: Use image node_id 4 for calling the LocalSagemakerClient obj's underlying member method code with example usage: obj.create_training_job(TrainingJobName, AlgorithmSpecification, OutputDataConfig, ResourceConfig, InputDataConfig, Environment) without return types