class_docstr: Client for interacting with the MLflow Deployments Server.
  
  Example:
  
  First, start the MLflow Deployments Server:
  
  .. code-block:: bash
  
      mlflow deployments start-server --config-path path/to/config.yaml
  
  Then, create a client and use it to interact with the server:
  
  .. code-block:: python
  
      from mlflow.deployments import get_deploy_client
  
      client = get_deploy_client("http://localhost:5000")
      endpoints = client.list_endpoints()
      assert [e.dict() for e in endpoints] == [
          {
              "name": "chat",
              "endpoint_type": "llm/v1/chat",
              "model": {"name": "gpt-3.5-turbo", "provider": "openai"},
              "endpoint_url": "http://localhost:5000/gateway/chat/invocations",
          },
      ]
docstr: Gets a specified endpoint configured for the MLflow Deployments Server.
  
  Args:
      endpoint: The name of the endpoint to retrieve.
  
  Returns:
      An `Endpoint` object representing the endpoint.
  
  Example:
  
  .. code-block:: python
  
      from mlflow.deployments import get_deploy_client
  
      client = get_deploy_client("http://localhost:5000")
      endpoint = client.get_endpoint(endpoint="chat")
      assert endpoint.dict() == {
          "name": "chat",
          "endpoint_type": "llm/v1/chat",
          "model": {"name": "gpt-3.5-turbo", "provider": "openai"},
          "endpoint_url": "http://localhost:5000/gateway/chat/invocations",
      }
docstr_tok: ["Gets","a","specified","endpoint","configured","for","the","MLflow","Deployments","Server",".","Args",":","endpoint",":","The","name","of","the","endpoint","to","retrieve",".","Returns",":","An","`","Endpoint","`","object","representing","the","endpoint",".","Example",":","..","code-block",":",":","python","from","mlflow.deployments","import","get_deploy_client","client","=","get_deploy_client","(","``","http",":","\/\/localhost:5000","''",")","endpoint","=","client.get_endpoint","(","endpoint=","''","chat","''",")","assert","endpoint.dict","(",")","==","{","``","name","''",":","``","chat","''",",","``","endpoint_type","''",":","``","llm\/v1\/chat","''",",","``","model","''",":","{","``","name","''",":","``","gpt-3.5-turbo","''",",","``","provider","''",":","``","openai","''","}",",","``","endpoint_url","''",":","``","http",":","\/\/localhost:5000\/gateway\/chat\/invocations","''",",","}"]
code: def get_endpoint(self, endpoint) -> "Endpoint":
      """
      Gets a specified endpoint configured for the MLflow Deployments Server.
  
      Args:
          endpoint: The name of the endpoint to retrieve.
  
      Returns:
          An `Endpoint` object representing the endpoint.
  
      Example:
  
      .. code-block:: python
  
          from mlflow.deployments import get_deploy_client
  
          client = get_deploy_client("http://localhost:5000")
          endpoint = client.get_endpoint(endpoint="chat")
          assert endpoint.dict() == {
              "name": "chat",
              "endpoint_type": "llm/v1/chat",
              "model": {"name": "gpt-3.5-turbo", "provider": "openai"},
              "endpoint_url": "http://localhost:5000/gateway/chat/invocations",
          }
      """
      route = join_paths(
          MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, endpoint
      )
      response = self._call_endpoint("GET", route)
      return Endpoint(
          **{
              **response,
              "endpoint_url": resolve_endpoint_url(
                  self.target_uri, response["endpoint_url"]
              ),
          }
      )
  
imports: from typing import TYPE_CHECKING, Any, Dict, List, Optional
  import requests
  from mlflow import MlflowException
  from mlflow.deployments import BaseDeploymentClient
  from mlflow.deployments.constants import MLFLOW_DEPLOYMENT_CLIENT_REQUEST_RETRY_CODES, MLFLOW_DEPLOYMENT_PREDICT_TIMEOUT
  from mlflow.deployments.server.config import Endpoint
  from mlflow.deployments.server.constants import MLFLOW_DEPLOYMENTS_CRUD_ENDPOINT_BASE, MLFLOW_DEPLOYMENTS_ENDPOINTS_BASE, MLFLOW_DEPLOYMENTS_QUERY_SUFFIX
  from mlflow.deployments.utils import resolve_endpoint_url
  from mlflow.environment_variables import MLFLOW_HTTP_REQUEST_TIMEOUT
  from mlflow.protos.databricks_pb2 import BAD_REQUEST
  from mlflow.store.entities.paged_list import PagedList
  from mlflow.tracking._tracking_service.utils import _get_default_host_creds
  from mlflow.utils.annotations import experimental
  from mlflow.utils.rest_utils import augmented_raise_for_status, http_request
  from mlflow.utils.uri import join_paths
label_desc: Use image node_id 10 for calling the MlflowDeploymentClient obj's underlying member method code with example usage: obj.get_endpoint(endpoint) and returns: Endpoint