class_docstr: Client for interacting with OpenAI endpoints.
  
  Example:
  
  First, set up credentials for authentication:
  
  .. code-block:: bash
  
      export OPENAI_API_KEY=...
  
  .. seealso::
  
      See https://mlflow.org/docs/latest/python_api/openai/index.html for other authentication methods.
  
  Then, create a deployment client and use it to interact with OpenAI endpoints:
  
  .. code-block:: python
  
      from mlflow.deployments import get_deploy_client
  
      client = get_deploy_client("openai")
      client.predict(
          endpoint="gpt-3.5-turbo",
          inputs={
              "messages": [
                  {"role": "user", "content": "Hello!"},
              ],
          },
      )
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docstr: .. warning::
  
      This method is not implemented for `OpenAIDeploymentClient`.
docstr_tok: ["..","warning",":",":","This","method","is","not","implemented","for","`","OpenAIDeploymentClient","`","."]
code: def delete_endpoint(self, endpoint):
      """
      .. warning::
  
          This method is not implemented for `OpenAIDeploymentClient`.
      """
      raise NotImplementedError
  
imports: import logging
  import os
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.utils.openai_utils import REQUEST_URL_CHAT, _OAITokenHolder, _OpenAIApiConfig, _OpenAIEnvVar
  from mlflow.utils.rest_utils import augmented_raise_for_status
  from mlflow.deployments import BaseDeploymentClient
label_desc: Use image node_id 9 for calling the OpenAIDeploymentClient obj's underlying member method code with example usage: obj.delete_endpoint(endpoint) without return types