class_docstr: Fetch trial component data and make them accessible for analytics.
docstr: Reshape trial component input/output artifacts to a pandas column.
  
  Args:
      artifacts: trial component input/output artifacts
  Returns:
      dict: Key: artifacts name, Value: artifacts value
docstr_tok: ["Reshape","trial","component","input\/output","artifacts","to","a","pandas","column",".","Args",":","artifacts",":","trial","component","input\/output","artifacts","Returns",":","dict",":","Key",":","artifacts","name",",","Value",":","artifacts","value"]
code: def _reshape_artifacts(self, artifacts, _artifact_names):
      """Reshape trial component input/output artifacts to a pandas column.
  
      Args:
          artifacts: trial component input/output artifacts
      Returns:
          dict: Key: artifacts name, Value: artifacts value
      """
      out = OrderedDict()
      for name, value in sorted(artifacts.items()):
          if _artifact_names and (name not in _artifact_names):
              continue
          out["{} - {}".format(name, "MediaType")] = value.get(
              "MediaType"
          )
          out["{} - {}".format(name, "Value")] = value.get("Value")
      return out
  
imports: from __future__ import print_function, absolute_import
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict, OrderedDict
  import datetime
  import logging
  from six import with_metaclass
  from sagemaker.session import Session
  from sagemaker.utils import DeferredError
  from sagemaker.lineage import artifact
label_desc: Use image node_id 7 for calling the ExperimentAnalytics obj's underlying member method code with example usage: obj._reshape_artifacts(artifacts, _artifact_names) and returns: out