class_docstr: Flavor backend implementation for the generic python models.
docstr: Generate predictions using generic python model saved with MLflow. The expected format of
  the input JSON is the MLflow scoring format.
  Return the prediction results as a JSON.
code: def predict(
      self,
      model_uri,
      input_path,
      output_path,
      content_type,
      pip_requirements_override=None,
  ):
      """
      Generate predictions using generic python model saved with MLflow. The expected format of
      the input JSON is the MLflow scoring format.
      Return the prediction results as a JSON.
      """
      local_path = _download_artifact_from_uri(model_uri)
      # NB: Absolute windows paths do not work with mlflow apis, use file uri to ensure
      # platform compatibility.
      local_uri = path_to_local_file_uri(local_path)
  
      if self._env_manager != _EnvManager.LOCAL:
          predict_cmd = [
              "python",
              _mlflow_pyfunc_backend_predict.__file__,
              "--model-uri",
              str(local_uri),
              "--content-type",
              shlex.quote(str(content_type)),
          ]
          if input_path:
              predict_cmd += [
                  "--input-path",
                  shlex.quote(str(input_path)),
              ]
          if output_path:
              predict_cmd += [
                  "--output-path",
                  shlex.quote(str(output_path)),
              ]
  
          if (
              pip_requirements_override
              and self._env_manager == _EnvManager.CONDA
          ):
              # Conda use = instead of == for version pinning
              pip_requirements_override = [
                  l.replace("==", "=")
                  for l in pip_requirements_override
              ]
  
          environment = self.prepare_env(
              local_path,
              pip_requirements_override=pip_requirements_override,
          )
  
          try:
              environment.execute(" ".join(predict_cmd))
          except ShellCommandException as e:
              raise MlflowException(
                  f"{e}\n\nAn exception occurred while running model prediction within a "
                  f"{self._env_manager} environment. You can find the error message "
                  f"from the prediction subprocess by scrolling above."
              ) from None
      else:
          if pip_requirements_override:
              raise MlflowException(
                  "`pip_requirements_override` is not supported for local env manager."
                  "Please use conda or virtualenv instead."
              )
          scoring_server._predict(
              local_uri, input_path, output_path, content_type
          )
  
imports: import ctypes
  import logging
  import os
  import pathlib
  import posixpath
  import shlex
  import signal
  import subprocess
  import sys
  import warnings
  from pathlib import Path
  from mlflow.environment_variables import MLFLOW_DISABLE_ENV_CREATION
  from mlflow.exceptions import MlflowException
  from mlflow.models import FlavorBackend
  from mlflow.models.container import ENABLE_MLSERVER
  from mlflow.models.docker_utils import SETUP_MINICONDA, SETUP_PYENV_AND_VIRTUALENV, _build_image, _generate_dockerfile_content, _get_mlflow_install_step
  from mlflow.pyfunc import ENV, _extract_conda_env, _mlflow_pyfunc_backend_predict, mlserver, scoring_server
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils import env_manager
  from mlflow.utils.conda import get_conda_bin_executable, get_or_create_conda_env
  from mlflow.utils.environment import Environment
  from mlflow.utils.file_utils import get_or_create_nfs_tmp_dir, get_or_create_tmp_dir, path_to_local_file_uri
  from mlflow.utils.nfs_on_spark import get_nfs_cache_root_dir
  from mlflow.utils.process import ShellCommandException, cache_return_value_per_process
  from mlflow.utils.virtualenv import _get_or_create_virtualenv, _get_pip_install_mlflow
  from mlflow.version import VERSION
label_desc: Use image node_id 3 for calling the PyFuncBackend obj's underlying member method code with example usage: obj.predict(model_uri, input_path, output_path, content_type, pip_requirements_override) without return types