class_docstr: Creates a dataframe containing the lineage assoociations of a SageMaker object.
docstr: Generate a dataframe containing all incoming and outgoing lineage entities.
  
    Examples:
    .. code-block:: python
  
        viz = LineageTableVisualizer(sagemaker_session)
        df = viz.show(training_job_name=training_job_name)
        # in a notebook
        display(df.to_html())
  
  Args:
      trial_component_name (str, optional): Name of  a trial component. Defaults to None.
      training_job_name (str, optional): Name of a training job. Defaults to None.
      processing_job_name (str, optional): Name of a processing job. Defaults to None.
      pipeline_execution_step (obj, optional): Pipeline execution step. Defaults to None.
      model_package_arn (str, optional): Model package arn. Defaults to None.
      endpoint_arn (str, optional): Endpoint arn. Defaults to None.
      artifact_arn (str, optional): Artifact arn. Defaults to None.
      context_arn (str, optional): Context arn. Defaults to None.
      actions_arn (str, optional): Action arn. Defaults to None.
  
  Returns:
      DataFrame: Pandas dataframe containing lineage associations.
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code: def show(
      self,
      trial_component_name: Optional[str] = None,
      training_job_name: Optional[str] = None,
      processing_job_name: Optional[str] = None,
      pipeline_execution_step: Optional[object] = None,
      model_package_arn: Optional[str] = None,
      endpoint_arn: Optional[str] = None,
      artifact_arn: Optional[str] = None,
      context_arn: Optional[str] = None,
      actions_arn: Optional[str] = None,
  ) -> DataFrame:
      """Generate a dataframe containing all incoming and outgoing lineage entities.
  
        Examples:
        .. code-block:: python
  
            viz = LineageTableVisualizer(sagemaker_session)
            df = viz.show(training_job_name=training_job_name)
            # in a notebook
            display(df.to_html())
  
      Args:
          trial_component_name (str, optional): Name of  a trial component. Defaults to None.
          training_job_name (str, optional): Name of a training job. Defaults to None.
          processing_job_name (str, optional): Name of a processing job. Defaults to None.
          pipeline_execution_step (obj, optional): Pipeline execution step. Defaults to None.
          model_package_arn (str, optional): Model package arn. Defaults to None.
          endpoint_arn (str, optional): Endpoint arn. Defaults to None.
          artifact_arn (str, optional): Artifact arn. Defaults to None.
          context_arn (str, optional): Context arn. Defaults to None.
          actions_arn (str, optional): Action arn. Defaults to None.
  
      Returns:
          DataFrame: Pandas dataframe containing lineage associations.
      """
      start_arn: str = None
  
      if trial_component_name:
          start_arn = self._get_start_arn_from_trial_component_name(
              trial_component_name
          )
      elif training_job_name:
          trial_component_name = training_job_name + "-aws-training-job"
          start_arn = self._get_start_arn_from_trial_component_name(
              trial_component_name
          )
      elif processing_job_name:
          trial_component_name = (
              processing_job_name + "-aws-processing-job"
          )
          start_arn = self._get_start_arn_from_trial_component_name(
              trial_component_name
          )
      elif pipeline_execution_step:
          start_arn = self._get_start_arn_from_pipeline_execution_step(
              pipeline_execution_step
          )
      elif model_package_arn:
          start_arn = self._get_start_arn_from_model_package_arn(
              model_package_arn
          )
      elif endpoint_arn:
          start_arn = self._get_start_arn_from_endpoint_arn(
              endpoint_arn
          )
      elif artifact_arn:
          start_arn = artifact_arn
      elif context_arn:
          start_arn = context_arn
      elif actions_arn:
          start_arn = actions_arn
  
      return self._get_associations_dataframe(start_arn)
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, Any, Iterator
  import pandas
  from pandas import DataFrame
  from sagemaker.lineage._api_types import AssociationSummary
  from sagemaker.lineage.association import Association
label_desc: Use image node_id 2 for calling the LineageTableVisualizer obj's underlying member method code with example usage: obj.show(trial_component_name, training_job_name, processing_job_name, pipeline_execution_step, model_package_arn, endpoint_arn, artifact_arn, context_arn, actions_arn) and returns: self