class_docstr: Create a Debugger hook configuration object to save the tensor for debugging.
  
  DebuggerHookConfig provides options to customize how debugging
  information is emitted and saved. This high-level DebuggerHookConfig class
  runs based on the `smdebug.SaveConfig
  <https://github.com/awslabs/sagemaker-debugger/blob/master/docs/
  api.md#saveconfig>`_ class.
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docstr: Initialize the DebuggerHookConfig instance.
  
  Args:
      s3_output_path (str or PipelineVariable): Optional. The location in Amazon S3 to
          store the output tensors. The default Debugger output path is created under the
          default output path of the :class:`~sagemaker.estimator.Estimator` class.
          For example,
          s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
      container_local_output_path (str or PipelineVariable): Optional. The local path
          in the container.
      hook_parameters (dict[str, str] or dict[str, PipelineVariable]): Optional.
          A dictionary of parameters.
      collection_configs ([sagemaker.debugger.CollectionConfig]): Required. A list
          of :class:`~sagemaker.debugger.CollectionConfig` objects to be saved
          at the **s3_output_path**.
  
  **Example of creating a DebuggerHookConfig object:**
  
  .. code-block:: python
  
      from sagemaker.debugger import CollectionConfig, DebuggerHookConfig
  
      collection_configs=[
          CollectionConfig(name="tensor_collection_1")
          CollectionConfig(name="tensor_collection_2")
          ...
          CollectionConfig(name="tensor_collection_n")
      ]
  
      hook_config = DebuggerHookConfig(
          collection_configs=collection_configs
      )
code: def __init__(
      self,
      s3_output_path: Optional[Union[str, PipelineVariable]] = None,
      container_local_output_path: Optional[
          Union[str, PipelineVariable]
      ] = None,
      hook_parameters: Optional[
          Dict[str, Union[str, PipelineVariable]]
      ] = None,
      collection_configs: Optional[List["CollectionConfig"]] = None,
  ):
      """Initialize the DebuggerHookConfig instance.
  
      Args:
          s3_output_path (str or PipelineVariable): Optional. The location in Amazon S3 to
              store the output tensors. The default Debugger output path is created under the
              default output path of the :class:`~sagemaker.estimator.Estimator` class.
              For example,
              s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
          container_local_output_path (str or PipelineVariable): Optional. The local path
              in the container.
          hook_parameters (dict[str, str] or dict[str, PipelineVariable]): Optional.
              A dictionary of parameters.
          collection_configs ([sagemaker.debugger.CollectionConfig]): Required. A list
              of :class:`~sagemaker.debugger.CollectionConfig` objects to be saved
              at the **s3_output_path**.
  
      **Example of creating a DebuggerHookConfig object:**
  
      .. code-block:: python
  
          from sagemaker.debugger import CollectionConfig, DebuggerHookConfig
  
          collection_configs=[
              CollectionConfig(name="tensor_collection_1")
              CollectionConfig(name="tensor_collection_2")
              ...
              CollectionConfig(name="tensor_collection_n")
          ]
  
          hook_config = DebuggerHookConfig(
              collection_configs=collection_configs
          )
  
      """
      self.s3_output_path = s3_output_path
      self.container_local_output_path = container_local_output_path
      self.hook_parameters = hook_parameters
      self.collection_configs = collection_configs
  
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 DebuggerHookConfig object from inherited base classes: object with example: obj = DebuggerHookConfig(s3_output_path, container_local_output_path, hook_parameters, collection_configs)