code: def get_default_pip_requirements(is_spark_connect_model=False):
      """
      :return: A list of default pip requirements for MLflow Models produced by this flavor.
               Calls to :func:`save_model()` and :func:`log_model()` produce a pip environment
               that, at minimum, contains these requirements.
      """
      import pyspark
  
      # Strip the suffix from `dev` versions of PySpark, which are not
      # available for installation from Anaconda or PyPI
      pyspark_req_str = (
          "pyspark[connect]" if is_spark_connect_model else "pyspark"
      )
      pyspark_req = re.sub(
          r"(\.?)dev.*$", "", _get_pinned_requirement(pyspark_req_str)
      )
      reqs = [pyspark_req]
      if Version(pyspark.__version__) < Version("3.4"):
          # Versions of PySpark < 3.4 are incompatible with pandas >= 2
          reqs.append("pandas<2")
  
      if is_spark_connect_model:
          reqs.extend(
              [
                  # Spark connect ML Model uses spark torch distributor to train model
                  _get_pinned_requirement("torch"),
                  # Spark connect ML Model saves feature transformers as sklearn transformer format.
                  _get_pinned_requirement(
                      "scikit-learn", module="sklearn"
                  ),
                  # Spark connect ML evaluators depend on torcheval package.
                  _get_pinned_requirement("torcheval"),
              ]
          )
      return reqs
  
imports: import logging
  import os
  import posixpath
  import re
  import shutil
  from typing import Any, Dict, Optional
  import pandas
  import yaml
  from packaging.version import Version
  import mlflow
  from mlflow import environment_variables, mleap, pyfunc
  from mlflow.environment_variables import MLFLOW_DFS_TMP
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelInputExample, ModelSignature, infer_signature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _LOG_MODEL_INFER_SIGNATURE_WARNING_TEMPLATE
  from mlflow.models.utils import _Example, _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.store.artifact.databricks_artifact_repo import DatabricksArtifactRepository
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri, _get_root_uri_and_artifact_path
  from mlflow.utils import _get_fully_qualified_class_name, databricks_utils
  from mlflow.utils.autologging_utils import autologging_integration, safe_patch
  from mlflow.utils.class_utils import _get_class_from_string
  from mlflow.utils.docstring_utils import LOG_MODEL_PARAM_DOCS, format_docstring
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import TempDir, get_total_file_size, shutil_copytree_without_file_permissions, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration_from_uri, _validate_and_copy_code_paths
  from mlflow.utils.requirements_utils import _get_pinned_requirement
  from mlflow.utils.uri import append_to_uri_path, dbfs_hdfs_uri_to_fuse_path, generate_tmp_dfs_path, get_databricks_profile_uri_from_artifact_uri, is_databricks_acled_artifacts_uri, is_local_uri, is_valid_dbfs_uri
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_pip_requirements(is_spark_connect_model) and returns: reqs