class_docstr: Create a tensor ouput configuration object for debugging visualizations on TensorBoard.
class_docstr_tok: ["Create","a","tensor","ouput","configuration","object","for","debugging","visualizations","on","TensorBoard","."]
docstr: Initialize the TensorBoardOutputConfig instance.
  
  Args:
      s3_output_path (str or PipelineVariable): Optional. The location in Amazon S3
          to store the output.
      container_local_output_path (str or PipelineVariable): Optional. The local path
          in the container.
code: def __init__(
      self,
      s3_output_path: Union[str, PipelineVariable],
      container_local_output_path: Optional[
          Union[str, PipelineVariable]
      ] = None,
  ):
      """Initialize the TensorBoardOutputConfig instance.
  
      Args:
          s3_output_path (str or PipelineVariable): Optional. The location in Amazon S3
              to store the output.
          container_local_output_path (str or PipelineVariable): Optional. The local path
              in the container.
  
      """
      self.s3_output_path = s3_output_path
      self.container_local_output_path = container_local_output_path
  
imports: from __future__ import absolute_import
  from abc import ABC
  from typing import Union, Optional, List, Dict
  import attr
  import smdebug_rulesconfig
  from sagemaker import image_uris
  from sagemaker.utils import build_dict, name_from_base
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.debugger.profiler_constants import DETAIL_PROF_PROCESSING_DEFAULT_INSTANCE_TYPE, DETAIL_PROF_PROCESSING_DEFAULT_VOLUME_SIZE
label_desc: Use image node_id 1 to create a new TensorBoardOutputConfig object from inherited base classes: object with example: obj = TensorBoardOutputConfig(s3_output_path, container_local_output_path)