class_docstr: JumpStartEstimator class.
  
  This class sets defaults based on the model ID and version.
docstr: Start training job by calling base ``Estimator`` class ``fit`` method.
  
  Any field set to ``None`` does not get passed to the parent class method.
  
  Args:
      inputs (Optional[Union[str, dict, sagemaker.inputs.TrainingInput, sagemaker.inputs.FileSystemInput]]):
          Information about the training data. This can be one of four types:
  
          * (str) the S3 location where training data is saved, or a file:// path in
              local mode.
          * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput] or
              dict[str, sagemaker.inputs.FileSystemInput]) If using multiple channels for
              training data, you can specify a dict mapping channel names to strings or
              :func:`~sagemaker.inputs.TrainingInput` objects or
              :func:`~sagemaker.inputs.FileSystemInput` objects.
          * (sagemaker.inputs.TrainingInput) - channel configuration for S3 data sources
              that can provide additional information as well as the path to the training
              dataset.
              See :func:`sagemaker.inputs.TrainingInput` for full details.
          * (sagemaker.inputs.FileSystemInput) - channel configuration for
              a file system data source that can provide additional information as well as
              the path to the training dataset.
  
  
          (Default: None).
      wait (Optional[bool]): Whether the call should wait until the job completes.
          (Default: True).
      logs (Optional[List[str]]): A list of strings specifying which logs to print. Acceptable
          strings are "All", "None", "Training", or "Rules". To maintain backwards
          compatibility, boolean values are also accepted and converted to strings.
          Only meaningful when wait is True. (Default: None).
      job_name (Optional[str]): Training job name. If not specified, the estimator generates
          a default job name based on the training image name and current timestamp.
          (Default: None).
      experiment_config (Optional[dict[str, str]]): Experiment management configuration.
          Optionally, the dict can contain four keys:
          'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'..
          The behavior of setting these keys is as follows:
          * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
          automatically created and the job's Trial Component associated with the Trial.
          * If `TrialName` is supplied and the Trial already exists the job's Trial Component
          will be associated with the Trial.
          * If both `ExperimentName` and `TrialName` are not supplied the trial component
          will be unassociated.
          * `TrialComponentDisplayName` is used for display in Studio.
          * Both `ExperimentName` and `TrialName` will be ignored if the Estimator instance
          is built with :class:`~sagemaker.workflow.pipeline_context.PipelineSession`.
          However, the value of `TrialComponentDisplayName` is honored for display in Studio.
          (Default: None).
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code: def fit(
      self,
      inputs: Optional[
          Union[str, Dict, TrainingInput, FileSystemInput]
      ] = None,
      wait: Optional[bool] = True,
      logs: Optional[str] = None,
      job_name: Optional[str] = None,
      experiment_config: Optional[Dict[str, str]] = None,
  ) -> None:
      """Start training job by calling base ``Estimator`` class ``fit`` method.
  
      Any field set to ``None`` does not get passed to the parent class method.
  
      Args:
          inputs (Optional[Union[str, dict, sagemaker.inputs.TrainingInput, sagemaker.inputs.FileSystemInput]]):
              Information about the training data. This can be one of four types:
  
              * (str) the S3 location where training data is saved, or a file:// path in
                  local mode.
              * (dict[str, str] or dict[str, sagemaker.inputs.TrainingInput] or
                  dict[str, sagemaker.inputs.FileSystemInput]) If using multiple channels for
                  training data, you can specify a dict mapping channel names to strings or
                  :func:`~sagemaker.inputs.TrainingInput` objects or
                  :func:`~sagemaker.inputs.FileSystemInput` objects.
              * (sagemaker.inputs.TrainingInput) - channel configuration for S3 data sources
                  that can provide additional information as well as the path to the training
                  dataset.
                  See :func:`sagemaker.inputs.TrainingInput` for full details.
              * (sagemaker.inputs.FileSystemInput) - channel configuration for
                  a file system data source that can provide additional information as well as
                  the path to the training dataset.
  
  
              (Default: None).
          wait (Optional[bool]): Whether the call should wait until the job completes.
              (Default: True).
          logs (Optional[List[str]]): A list of strings specifying which logs to print. Acceptable
              strings are "All", "None", "Training", or "Rules". To maintain backwards
              compatibility, boolean values are also accepted and converted to strings.
              Only meaningful when wait is True. (Default: None).
          job_name (Optional[str]): Training job name. If not specified, the estimator generates
              a default job name based on the training image name and current timestamp.
              (Default: None).
          experiment_config (Optional[dict[str, str]]): Experiment management configuration.
              Optionally, the dict can contain four keys:
              'ExperimentName', 'TrialName', 'TrialComponentDisplayName' and 'RunName'..
              The behavior of setting these keys is as follows:
              * If `ExperimentName` is supplied but `TrialName` is not a Trial will be
              automatically created and the job's Trial Component associated with the Trial.
              * If `TrialName` is supplied and the Trial already exists the job's Trial Component
              will be associated with the Trial.
              * If both `ExperimentName` and `TrialName` are not supplied the trial component
              will be unassociated.
              * `TrialComponentDisplayName` is used for display in Studio.
              * Both `ExperimentName` and `TrialName` will be ignored if the Estimator instance
              is built with :class:`~sagemaker.workflow.pipeline_context.PipelineSession`.
              However, the value of `TrialComponentDisplayName` is honored for display in Studio.
              (Default: None).
      """
  
      estimator_fit_kwargs = get_fit_kwargs(
          model_id=self.model_id,
          model_version=self.model_version,
          region=self.region,
          inputs=inputs,
          wait=wait,
          logs=logs,
          job_name=job_name,
          experiment_config=experiment_config,
          tolerate_vulnerable_model=self.tolerate_vulnerable_model,
          tolerate_deprecated_model=self.tolerate_deprecated_model,
          sagemaker_session=self.sagemaker_session,
      )
  
      return super(JumpStartEstimator, self).fit(
          **estimator_fit_kwargs.to_kwargs_dict()
      )
  
imports: from __future__ import absolute_import
  from typing import Dict, List, Optional, Union
  from sagemaker import session
  from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
  from sagemaker.base_deserializers import BaseDeserializer
  from sagemaker.base_serializers import BaseSerializer
  from sagemaker.debugger.debugger import DebuggerHookConfig, RuleBase, TensorBoardOutputConfig
  from sagemaker.debugger.profiler_config import ProfilerConfig
  from sagemaker.estimator import Estimator
  from sagemaker.explainer.explainer_config import ExplainerConfig
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.instance_group import InstanceGroup
  from sagemaker.jumpstart.accessors import JumpStartModelsAccessor
  from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION
  from sagemaker.jumpstart.enums import JumpStartScriptScope
  from sagemaker.jumpstart.exceptions import INVALID_MODEL_ID_ERROR_MSG
  from sagemaker.jumpstart.factory.estimator import get_deploy_kwargs, get_fit_kwargs, get_init_kwargs
  from sagemaker.jumpstart.factory.model import get_default_predictor
  from sagemaker.jumpstart.utils import is_valid_model_id, resolve_model_sagemaker_config_field
  from sagemaker.utils import stringify_object, format_tags, Tags
  from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
  from sagemaker.predictor import PredictorBase
  from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 2 for calling the JumpStartEstimator obj's underlying member method code with example usage: obj.fit(inputs, wait, logs, job_name, experiment_config) and returns: super