code: def _build_mlflow_run_cmd(
      uri,
      entry_point,
      docker_args,
      storage_dir,
      env_manager,
      run_id,
      parameters,
  ):
      """
      Build and return an array containing an ``mlflow run`` command that can be invoked to locally
      run the project at the specified URI.
      """
      mlflow_run_arr = [
          "mlflow",
          "run",
          uri,
          "-e",
          entry_point,
          "--run-id",
          run_id,
      ]
      if docker_args is not None:
          for key, value in docker_args.items():
              args = (
                  key if isinstance(value, bool) else f"{key}={value}"
              )
              mlflow_run_arr.extend(["--docker-args", args])
      if storage_dir is not None:
          mlflow_run_arr.extend(["--storage-dir", storage_dir])
      mlflow_run_arr.extend(["--env-manager", env_manager])
      for key, value in parameters.items():
          mlflow_run_arr.extend(["-P", f"{key}={value}"])
      return mlflow_run_arr
  
imports: import logging
  import os
  import platform
  import posixpath
  import subprocess
  import sys
  from pathlib import Path
  import mlflow
  from mlflow import tracking
  from mlflow.environment_variables import MLFLOW_KERBEROS_TICKET_CACHE, MLFLOW_KERBEROS_USER, MLFLOW_PYARROW_EXTRA_CONF
  from mlflow.exceptions import MlflowException
  from mlflow.projects import env_type
  from mlflow.projects.backend.abstract_backend import AbstractBackend
  from mlflow.projects.submitted_run import LocalSubmittedRun
  from mlflow.projects.utils import MLFLOW_DOCKER_WORKDIR_PATH, MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG, PROJECT_BUILD_IMAGE, PROJECT_DOCKER_ARGS, PROJECT_DOCKER_AUTH, PROJECT_ENV_MANAGER, PROJECT_STORAGE_DIR, PROJECT_SYNCHRONOUS, fetch_and_validate_project, get_entry_point_command, get_or_create_run, get_run_env_vars, load_project
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.store.artifact.azure_blob_artifact_repo import AzureBlobArtifactRepository
  from mlflow.store.artifact.gcs_artifact_repo import GCSArtifactRepository
  from mlflow.store.artifact.hdfs_artifact_repo import HdfsArtifactRepository
  from mlflow.store.artifact.local_artifact_repo import LocalArtifactRepository
  from mlflow.store.artifact.s3_artifact_repo import S3ArtifactRepository
  from mlflow.utils import env_manager
  from mlflow.utils.conda import get_or_create_conda_env
  from mlflow.utils.databricks_utils import get_databricks_env_vars, is_in_databricks_runtime
  from mlflow.utils.environment import _PythonEnv
  from mlflow.utils.file_utils import get_or_create_nfs_tmp_dir
  from mlflow.utils.mlflow_tags import MLFLOW_PROJECT_ENV
  from mlflow.utils.virtualenv import _PYENV_ROOT_DIR, _VIRTUALENV_ENVS_DIR, _create_virtualenv, _get_mlflow_virtualenv_root, _get_virtualenv_extra_env_vars, _get_virtualenv_name, _install_python
label_desc: Use image node_id 3 for calling a global function with example usage: _build_mlflow_run_cmd(uri, entry_point, docker_args, storage_dir, env_manager, run_id, parameters) and returns: mlflow_run_arr