code: def load_model(model_uri, dst_path=None):
      """Load a fastai model from a local file or a run.
  
      Args:
          model_uri: The location, in URI format, of the MLflow model. For example:
  
              - ``/Users/me/path/to/local/model``
              - ``relative/path/to/local/model``
              - ``s3://my_bucket/path/to/model``
              - ``runs:/<mlflow_run_id>/run-relative/path/to/model``
  
              For more information about supported URI schemes, see
              `Referencing Artifacts <https://www.mlflow.org/docs/latest/tracking.html#
              artifact-locations>`_.
          dst_path: The local filesystem path to which to download the model artifact.
              This directory must already exist. If unspecified, a local output
              path will be created.
  
      Returns:
          A fastai model (an instance of `fastai.Learner`_).
  
      .. code-block:: python
          :caption: Example
  
          import mlflow.fastai
  
          # Define the Learner model
          model = ...
  
          # log the fastai Leaner model
          with mlflow.start_run() as run:
              model.fit(epochs, learning_rate)
              mlflow.fastai.log_model(model, "model")
  
          # Load the model for scoring
          model_uri = f"runs:/{run.info.run_id}/model"
          loaded_model = mlflow.fastai.load_model(model_uri)
          results = loaded_model.predict(predict_data)
      """
      local_model_path = _download_artifact_from_uri(
          artifact_uri=model_uri, output_path=dst_path
      )
      flavor_conf = _get_flavor_configuration(
          model_path=local_model_path, flavor_name=FLAVOR_NAME
      )
      _add_code_from_conf_to_system_path(local_model_path, flavor_conf)
  
      model_file_path = os.path.join(
          local_model_path, flavor_conf.get("data", "model.fastai")
      )
      return _load_model(path=model_file_path)
  
imports: import logging
  import os
  import tempfile
  from pathlib import Path
  from typing import Any, Dict, Optional
  import numpy
  import pandas
  import yaml
  import mlflow.tracking
  from mlflow import pyfunc
  from mlflow.models import Model, ModelInputExample, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.autologging_utils import autologging_integration, batch_metrics_logger, log_fn_args_as_params, safe_patch
  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 get_total_file_size, write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 7 for calling a global function with example usage: load_model(model_uri, dst_path) and returns: _load_model