code: def get_deploy_client(target_uri=None):
      """Returns a subclass of :py:class:`mlflow.deployments.BaseDeploymentClient` exposing standard
      APIs for deploying models to the specified target. See available deployment APIs
      by calling ``help()`` on the returned object or viewing docs for
      :py:class:`mlflow.deployments.BaseDeploymentClient`. You can also run
      ``mlflow deployments help -t <target-uri>`` via the CLI for more details on target-specific
      configuration options.
  
      Args:
          target_uri: Optional URI of target to deploy to. If no target URI is provided, then
              MLflow will attempt to get the deployments target set via `get_deployments_target()` or
              `MLFLOW_DEPLOYMENTS_TARGET` environment variable.
  
      .. code-block:: python
          :caption: Example
  
          from mlflow.deployments import get_deploy_client
          import pandas as pd
  
          client = get_deploy_client("redisai")
          # Deploy the model stored at artifact path 'myModel' under run with ID 'someRunId'. The
          # model artifacts are fetched from the current tracking server and then used for deployment.
          client.create_deployment("spamDetector", "runs:/someRunId/myModel")
          # Load a CSV of emails and score it against our deployment
          emails_df = pd.read_csv("...")
          prediction_df = client.predict_deployment("spamDetector", emails_df)
          # List all deployments, get details of our particular deployment
          print(client.list_deployments())
          print(client.get_deployment("spamDetector"))
          # Update our deployment to serve a different model
          client.update_deployment("spamDetector", "runs:/anotherRunId/myModel")
          # Delete our deployment
          client.delete_deployment("spamDetector")
      """
      if not target_uri:
          try:
              target_uri = get_deployments_target()
          except MlflowException:
              _logger.info(
                  "No deployments target has been set. Please either set the MLflow deployments "
                  "target via `mlflow.deployments.set_deployments_target()` or set the environment "
                  "variable MLFLOW_DEPLOYMENTS_TARGET to the running deployment server's uri"
              )
              return None
      target = parse_target_uri(target_uri)
      plugin = plugin_store[target]
      for _, obj in inspect.getmembers(plugin):
          if inspect.isclass(obj):
              if (
                  issubclass(obj, BaseDeploymentClient)
                  and not obj == BaseDeploymentClient
              ):
                  return obj(target_uri)
  
file_path: mlflow/mlflow/deployments/interface.py
imports: import inspect
  from logging import Logger
  from mlflow.deployments.base import BaseDeploymentClient
  from mlflow.deployments.plugin_manager import DeploymentPlugins
  from mlflow.deployments.utils import get_deployments_target, parse_target_uri
  from mlflow.exceptions import MlflowException
label_desc: Use image node_id 1 for calling a global function with example usage: get_deploy_client(target_uri) and returns: None, obj