class_docstr: A ``FrameworkModel`` implementation for inference with TensorFlow Serving.
docstr: Placeholder docstring.
docstr_tok: ["Placeholder","docstring","."]
code: def _get_container_env(self):
      """Placeholder docstring."""
      if not self._container_log_level:
          return self.env
  
      if self._container_log_level not in self.LOG_LEVEL_MAP:
          logging.warning(
              "ignoring invalid container log level: %s",
              self._container_log_level,
          )
          return self.env
  
      env = dict(self.env)
      env[self.LOG_LEVEL_PARAM_NAME] = self.LOG_LEVEL_MAP[
          self._container_log_level
      ]
      return env
  
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, List, Dict
  import sagemaker
  from sagemaker import image_uris, s3, ModelMetrics
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.deprecations import removed_kwargs
  from sagemaker.drift_check_baselines import DriftCheckBaselines
  from sagemaker.metadata_properties import MetadataProperties
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import JSONSerializer
  from sagemaker.workflow import is_pipeline_variable
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import PipelineSession
  from sagemaker.utils import format_tags
label_desc: Use image node_id 6 for calling the TensorFlowModel obj's underlying member method code with example usage: obj._get_container_env() and returns: env, self, self