code: def create_deployment(
      flavor, model_uri, target, name, config, endpoint
  ):
      """
      Deploy the model at ``model_uri`` to the specified target.
  
      Additional plugin-specific arguments may also be passed to this command, via `-C key=value`
      """
      config_dict = _user_args_to_dict(config)
      client = interface.get_deploy_client(target)
  
      sig = signature(client.create_deployment)
      if "endpoint" in sig.parameters:
          deployment = client.create_deployment(
              name,
              model_uri,
              flavor,
              config=config_dict,
              endpoint=endpoint,
          )
      else:
          deployment = client.create_deployment(
              name, model_uri, flavor, config=config_dict
          )
      click.echo(
          "\n{} deployment {} is created".format(
              deployment["flavor"], deployment["name"]
          )
      )
  
imports: import json
  import sys
  from inspect import signature
  import click
  from mlflow.deployments import interface
  from mlflow.environment_variables import MLFLOW_DEPLOYMENTS_CONFIG
  from mlflow.utils import cli_args
  from mlflow.utils.annotations import experimental
  from mlflow.utils.proto_json_utils import NumpyEncoder, _get_jsonable_obj
label_desc: Use image node_id 3 for calling a global function with example usage: create_deployment(flavor, model_uri, target, name, config, endpoint) without return types