class_docstr: Helper class to create a model with added dependency wheels from an existing registered model.
  The `wheeled` model contains all the model dependencies as wheels stored as model artifacts.
  .. note::
      This utility only operates on a model that has been registered to the Model Registry.
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docstr: This method creates a requirements.txt file for the model dependencies if the file does not
  already exist. If uses the pip dependencies found in the conda.yaml env file.
  :param conda_env_path: Path to conda.yaml env file which contains the required pip
                          dependencies
  :param pip_requirements_path: Path where the new requirements.txt will be created.
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code: def _create_pip_requirement(
      self, conda_env_path, pip_requirements_path
  ):
      """
      This method creates a requirements.txt file for the model dependencies if the file does not
      already exist. If uses the pip dependencies found in the conda.yaml env file.
      :param conda_env_path: Path to conda.yaml env file which contains the required pip
                              dependencies
      :param pip_requirements_path: Path where the new requirements.txt will be created.
      """
      with open(conda_env_path) as f:
          conda_env = yaml.safe_load(f)
      pip_deps = _get_pip_deps(conda_env)
      _mlflow_additional_pip_env(pip_deps, pip_requirements_path)
  
imports: import os
  import platform
  import shutil
  import subprocess
  import sys
  import yaml
  import mlflow
  from mlflow import MlflowClient
  from mlflow.environment_variables import MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import BAD_REQUEST
  from mlflow.pyfunc.model import MLMODEL_FILE_NAME, Model
  from mlflow.store.artifact.utils.models import _parse_model_uri, get_model_name_and_version
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.annotations import experimental
  from mlflow.utils.environment import _REQUIREMENTS_FILE_NAME, _get_pip_deps, _mlflow_additional_pip_env, _overwrite_pip_deps
  from mlflow.utils.model_utils import _validate_and_prepare_target_save_path
  from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri
label_desc: Use image node_id 8 for calling the WheeledModel obj's underlying member method code with example usage: obj._create_pip_requirement(conda_env_path, pip_requirements_path) without return types