code: def run(
      self,
      project_uri,
      entry_point,
      params,
      version,
      backend_config,
      tracking_uri,
      experiment_id,
  ):
      work_dir = fetch_and_validate_project(
          project_uri, version, entry_point, params
      )
      project = load_project(work_dir)
      if MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG in backend_config:
          run_id = backend_config[MLFLOW_LOCAL_BACKEND_RUN_ID_CONFIG]
      else:
          run_id = None
      active_run = get_or_create_run(
          run_id,
          project_uri,
          experiment_id,
          work_dir,
          version,
          entry_point,
          params,
      )
      command_args = []
      command_separator = " "
      env_manager = backend_config[PROJECT_ENV_MANAGER]
      synchronous = backend_config[PROJECT_SYNCHRONOUS]
      docker_args = backend_config[PROJECT_DOCKER_ARGS]
      storage_dir = backend_config[PROJECT_STORAGE_DIR]
      build_image = backend_config[PROJECT_BUILD_IMAGE]
      docker_auth = backend_config[PROJECT_DOCKER_AUTH]
      # Select an appropriate env manager for the project env type
      if env_manager is None:
          env_manager = _env_type_to_env_manager(project.env_type)
      else:
          if (
              project.env_type == env_type.PYTHON
              and env_manager == _EnvManager.CONDA
          ):
              raise MlflowException.invalid_parameter_value(
                  "python_env project cannot be executed using conda. Set `--env-manager` to "
                  "'virtualenv' or 'local' to execute this project."
              )
  
      # If a docker_env attribute is defined in MLproject then it takes precedence over conda yaml
      # environments, so the project will be executed inside a docker container.
      if project.docker_env:
          from mlflow.projects.docker import (
              build_docker_image,
              validate_docker_env,
              validate_docker_installation,
          )
  
          tracking.MlflowClient().set_tag(
              active_run.info.run_id, MLFLOW_PROJECT_ENV, "docker"
          )
          validate_docker_env(project)
          validate_docker_installation()
          image = build_docker_image(
              work_dir=work_dir,
              repository_uri=project.name,
              base_image=project.docker_env.get("image"),
              run_id=active_run.info.run_id,
              build_image=build_image,
              docker_auth=docker_auth,
          )
          command_args += _get_docker_command(
              image=image,
              active_run=active_run,
              docker_args=docker_args,
              volumes=project.docker_env.get("volumes"),
              user_env_vars=project.docker_env.get("environment"),
          )
      # Synchronously create a conda environment (even though this may take some time)
      # to avoid failures due to multiple concurrent attempts to create the same conda env.
      elif env_manager == _EnvManager.VIRTUALENV:
          tracking.MlflowClient().set_tag(
              active_run.info.run_id, MLFLOW_PROJECT_ENV, "virtualenv"
          )
          command_separator = " && "
          if project.env_type == env_type.CONDA:
              python_env = _PythonEnv.from_conda_yaml(
                  project.env_config_path
              )
          else:
              python_env = (
                  _PythonEnv.from_yaml(project.env_config_path)
                  if project.env_config_path
                  else _PythonEnv()
              )
  
          if is_in_databricks_runtime():
              nfs_tmp_dir = get_or_create_nfs_tmp_dir()
              env_root = Path(nfs_tmp_dir) / "envs"
              pyenv_root = env_root / _PYENV_ROOT_DIR
              virtualenv_root = env_root / _VIRTUALENV_ENVS_DIR
              env_vars = _get_virtualenv_extra_env_vars(str(env_root))
          else:
              pyenv_root = None
              virtualenv_root = Path(_get_mlflow_virtualenv_root())
              env_vars = None
          python_bin_path = _install_python(
              python_env.python, pyenv_root=pyenv_root
          )
          work_dir_path = Path(work_dir)
          env_name = _get_virtualenv_name(python_env, work_dir_path)
          env_dir = virtualenv_root / env_name
          activate_cmd = _create_virtualenv(
              work_dir_path,
              python_bin_path,
              env_dir,
              python_env,
              extra_env=env_vars,
          )
          command_args += [activate_cmd]
      elif env_manager == _EnvManager.CONDA:
          tracking.MlflowClient().set_tag(
              active_run.info.run_id, MLFLOW_PROJECT_ENV, "conda"
          )
          command_separator = " && "
          conda_env = get_or_create_conda_env(project.env_config_path)
          command_args += conda_env.get_activate_command()
  
      # In synchronous mode, run the entry point command in a blocking fashion, sending status
      # updates to the tracking server when finished. Note that the run state may not be
      # persisted to the tracking server if interrupted
      if synchronous:
          command_args += get_entry_point_command(
              project, entry_point, params, storage_dir
          )
          command_str = command_separator.join(command_args)
          return _run_entry_point(
              command_str,
              work_dir,
              experiment_id,
              run_id=active_run.info.run_id,
          )
      # Otherwise, invoke `mlflow run` in a subprocess
      return _invoke_mlflow_run_subprocess(
          work_dir=work_dir,
          entry_point=entry_point,
          parameters=params,
          experiment_id=experiment_id,
          env_manager=env_manager,
          docker_args=docker_args,
          storage_dir=storage_dir,
          run_id=active_run.info.run_id,
      )
  
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 1 for calling the LocalBackend obj's underlying member method code with example usage: obj.run(project_uri, entry_point, params, version, backend_config, tracking_uri, experiment_id) and returns: _invoke_mlflow_run_subprocess, _run_entry_point