code: """
  The ``mlflow.pytorch`` module provides an API for logging and loading PyTorch models. This module
  exports PyTorch models with the following flavors:
  
  PyTorch (native) format
      This is the main flavor that can be loaded back into PyTorch.
  :py:mod:`mlflow.pyfunc`
      Produced for use by generic pyfunc-based deployment tools and batch inference.
  """
  import importlib
  import logging
  import os
  import yaml
  
  import cloudpickle
  import numpy as np
  import pandas as pd
  from distutils.version import LooseVersion
  import posixpath
  
  import mlflow
  import shutil
  import mlflow.pyfunc.utils as pyfunc_utils
  from mlflow import pyfunc
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model, ModelSignature
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import ModelInputExample, _save_example
  from mlflow.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST
  from mlflow.pytorch import pickle_module as mlflow_pytorch_pickle_module
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.annotations import experimental
  from mlflow.utils.environment import _mlflow_conda_env
  from mlflow.utils.file_utils import _copy_file_or_tree, TempDir
  from mlflow.utils.model_utils import _get_flavor_configuration
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.utils.autologging_utils import autologging_integration, safe_patch
  
  FLAVOR_NAME = "pytorch"
  
  _SERIALIZED_TORCH_MODEL_FILE_NAME = "model.pth"
  _PICKLE_MODULE_INFO_FILE_NAME = "pickle_module_info.txt"
  _EXTRA_FILES_KEY = "extra_files"
  _REQUIREMENTS_FILE_KEY = "requirements_file"
  
  _logger = logging.getLogger(__name__)
  
  
  def get_default_conda_env():
      """
      :return: The default Conda environment as a dictionary for MLflow Models produced by calls to
               :func:`save_model()` and :func:`log_model()`.
  
      .. code-block:: python
          :caption: Example
  
          import mlflow.pytorch
  
          # Log PyTorch model
          with mlflow.start_run() as run:
              mlflow.pytorch.log_model(model, "model")
  
          # Fetch the associated conda environment
          env = mlflow.pytorch.get_default_conda_env()
          print("conda env: {}".format(env))
  
      .. code-block:: text
          :caption: Output
  
          conda env {'name': 'mlflow-env',
                     'channels': ['defaults', 'conda-forge', 'pytorch'],
                     'dependencies': ['python=3.7.5', 'pytorch=1.5.1',
                                      'torchvision=0.6.1',
                                      'pip', {'pip': ['mlflow', 'cloudpickle==1.6.0']}]}
      """
      import torch
      import torchvision
  
      return _mlflow_conda_env(
          additional_conda_deps=[
              "pytorch={}".format(torch.__version__),
              "torchvision={}".format(torchvision.__version__),
          ],
          additional_pip_deps=[
              # We include CloudPickle in the default environment because
              # it's required by the default pickle module used by `save_model()`
              # and `log_model()`: `mlflow.pytorch.pickle_module`.
              "cloudpickle=={}".format(cloudpickle.__version__)
          ],
          additional_conda_channels=["pytorch"],
      )
  
  
  def log_model(
      pytorch_model,
      artifact_path,
      conda_env=None,
      code_paths=None,
      pickle_module=None,
      registered_model_name=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      await_registration_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
      requirements_file=None,
      extra_files=None,
      **kwargs
  ):
      """
      Log a PyTorch model as an MLflow artifact for the current run.
  
      :param pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of
                            ``torch.nn.Module``) or scripted model prepared via ``torch.jit.script``
                            or ``torch.jit.trace``.
  
                            The model accept a single ``torch.FloatTensor`` as
                            input and produce a single output tensor.
  
                            If saving an eager model, any code dependencies of the
                            model's class, including the class definition itself, should be
                            included in one of the following locations:
  
                            - The package(s) listed in the model's Conda environment, specified
                              by the ``conda_env`` parameter.
                            - One or more of the files specified by the ``code_paths`` parameter.
  
      :param artifact_path: Run-relative artifact path.
      :param conda_env: Path to a Conda environment file. If provided, this decsribes the environment
                        this model should be run in. At minimum, it should specify the dependencies
                        contained in :func:`get_default_conda_env()`. If ``None``, the default
                        :func:`get_default_conda_env()` environment is added to the model. The
                        following is an *example* dictionary representation of a Conda environment::
  
                          {
                              'name': 'mlflow-env',
                              'channels': ['defaults'],
                              'dependencies': [
                                  'python=3.7.0',
                                  'pytorch=0.4.1',
                                  'torchvision=0.2.1'
                              ]
                          }
  
      :param code_paths: A list of local filesystem paths to Python file dependencies (or directories
                         containing file dependencies). These files are *prepended* to the system
                         path when the model is loaded.
      :param pickle_module: The module that PyTorch should use to serialize ("pickle") the specified
                            ``pytorch_model``. This is passed as the ``pickle_module`` parameter
                            to ``torch.save()``. By default, this module is also used to
                            deserialize ("unpickle") the PyTorch model at load time.
      :param registered_model_name: (Experimental) If given, create a model version under
                                    ``registered_model_name``, also creating a registered model if one
                                    with the given name does not exist.
  
      :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
                        describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
                        The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
                        from datasets with valid model input (e.g. the training dataset with target
                        column omitted) and valid model output (e.g. model predictions generated on
                        the training dataset), for example:
  
                        .. code-block:: python
  
                          from mlflow.models.signature import infer_signature
                          train = df.drop_column("target_label")
                          predictions = ... # compute model predictions
                          signature = infer_signature(train, predictions)
      :param input_example: (Experimental) Input example provides one or several instances of valid
                            model input. The example can be used as a hint of what data to feed the
                            model. The given example will be converted to a Pandas DataFrame and then
                            serialized to json using the Pandas split-oriented format. Bytes are
                            base64-encoded.
  
      :param await_registration_for: Number of seconds to wait for the model version to finish
                              being created and is in ``READY`` status. By default, the function
                              waits for five minutes. Specify 0 or None to skip waiting.
  
      :param requirements_file: A string containing the path to requirements file. Remote URIs
                        are resolved to absolute filesystem paths.
                        For example, consider the following ``requirements_file`` string -
  
                        requirements_file = "s3://my-bucket/path/to/my_file"
  
                        In this case, the ``"my_file"`` requirements file is downloaded from S3.
  
                        If ``None``, no requirements file is added to the model.
  
      :param extra_files: A list containing the paths to corresponding extra files. Remote URIs
                        are resolved to absolute filesystem paths.
                        For example, consider the following ``extra_files`` list -
  
                        extra_files = ["s3://my-bucket/path/to/my_file1",
                                      "s3://my-bucket/path/to/my_file2"]
  
                        In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3.
  
                        If ``None``, no extra files are added to the model.
  
      :param kwargs: kwargs to pass to ``torch.save`` method.
  
      .. code-block:: python
          :caption: Example
  
          import numpy as np
          import torch
          import mlflow.pytorch
  
          class LinearNNModel(torch.nn.Module):
              def __init__(self):
                  super(LinearNNModel, self).__init__()
                  self.linear = torch.nn.Linear(1, 1)  # One in and one out
  
              def forward(self, x):
                  y_pred = self.linear(x)
                  return y_pred
  
          def gen_data():
              # Example linear model modified to use y = 2x
              # from https://github.com/hunkim/PyTorchZeroToAll
              # X training data, y labels
              X = torch.arange(1.0, 25.0).view(-1, 1)
              y = torch.from_numpy(np.array([x * 2 for x in X])).view(-1, 1)
              return X, y
  
          # Define model, loss, and optimizer
          model = LinearNNModel()
          criterion = torch.nn.MSELoss()
          optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
  
          # Training loop
          epochs = 250
          X, y = gen_data()
          for epoch in range(epochs):
              # Forward pass: Compute predicted y by passing X to the model
              y_pred = model(X)
  
              # Compute the loss
              loss = criterion(y_pred, y)
  
              # Zero gradients, perform a backward pass, and update the weights.
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
          # Log the model
          with mlflow.start_run() as run:
              mlflow.pytorch.log_model(model, "model")
  
              # convert to scripted model and log the model
              scripted_pytorch_model = torch.jit.script(model)
              mlflow.pytorch.log_model(scripted_pytorch_model, "scripted_model")
  
          # Fetch the logged model artifacts
          print("run_id: {}".format(run.info.run_id))
          for artifact_path in ["model/data", "scripted_model/data"]:
              artifacts = [f.path for f in MlflowClient().list_artifacts(run.info.run_id,
                          artifact_path)]
              print("artifacts: {}".format(artifacts))
  
      .. code-block:: text
          :caption: Output
  
          run_id: 1a1ec9e413ce48e9abf9aec20efd6f71
          artifacts: ['model/data/model.pth',
                      'model/data/pickle_module_info.txt']
          artifacts: ['scripted_model/data/model.pth',
                      'scripted_model/data/pickle_module_info.txt']
  
      .. figure:: ../_static/images/pytorch_logged_models.png
  
          PyTorch logged models
      """
      pickle_module = pickle_module or mlflow_pytorch_pickle_module
      Model.log(
          artifact_path=artifact_path,
          flavor=mlflow.pytorch,
          pytorch_model=pytorch_model,
          conda_env=conda_env,
          code_paths=code_paths,
          pickle_module=pickle_module,
          registered_model_name=registered_model_name,
          signature=signature,
          input_example=input_example,
          await_registration_for=await_registration_for,
          requirements_file=requirements_file,
          extra_files=extra_files,
          **kwargs,
      )
  
  
  def save_model(
      pytorch_model,
      path,
      conda_env=None,
      mlflow_model=None,
      code_paths=None,
      pickle_module=None,
      signature: ModelSignature = None,
      input_example: ModelInputExample = None,
      requirements_file=None,
      extra_files=None,
      **kwargs
  ):
      """
      Save a PyTorch model to a path on the local file system.
  
      :param pytorch_model: PyTorch model to be saved. Can be either an eager model (subclass of
                            ``torch.nn.Module``) or scripted model prepared via ``torch.jit.script``
                            or ``torch.jit.trace``.
  
                            The model accept a single ``torch.FloatTensor`` as
                            input and produce a single output tensor.
  
                            If saving an eager model, any code dependencies of the
                            model's class, including the class definition itself, should be
                            included in one of the following locations:
  
                            - The package(s) listed in the model's Conda environment, specified
                              by the ``conda_env`` parameter.
                            - One or more of the files specified by the ``code_paths`` parameter.
  
      :param path: Local path where the model is to be saved.
      :param conda_env: Either a dictionary representation of a Conda environment or the path to a
                        Conda environment yaml file. If provided, this decsribes the environment
                        this model should be run in. At minimum, it should specify the dependencies
                        contained in :func:`get_default_conda_env()`. If ``None``, the default
                        :func:`get_default_conda_env()` environment is added to the model. The
                        following is an *example* dictionary representation of a Conda environment::
  
                          {
                              'name': 'mlflow-env',
                              'channels': ['defaults'],
                              'dependencies': [
                                  'python=3.7.0',
                                  'pytorch=0.4.1',
                                  'torchvision=0.2.1'
                              ]
                          }
  
      :param mlflow_model: :py:mod:`mlflow.models.Model` this flavor is being added to.
      :param code_paths: A list of local filesystem paths to Python file dependencies (or directories
                         containing file dependencies). These files are *prepended* to the system
                         path when the model is loaded.
      :param pickle_module: The module that PyTorch should use to serialize ("pickle") the specified
                            ``pytorch_model``. This is passed as the ``pickle_module`` parameter
                            to ``torch.save()``. By default, this module is also used to
                            deserialize ("unpickle") the PyTorch model at load time.
  
      :param signature: (Experimental) :py:class:`ModelSignature <mlflow.models.ModelSignature>`
                        describes model input and output :py:class:`Schema <mlflow.types.Schema>`.
                        The model signature can be :py:func:`inferred <mlflow.models.infer_signature>`
                        from datasets with valid model input (e.g. the training dataset with target
                        column omitted) and valid model output (e.g. model predictions generated on
                        the training dataset), for example:
  
                        .. code-block:: python
  
                          from mlflow.models.signature import infer_signature
                          train = df.drop_column("target_label")
                          predictions = ... # compute model predictions
                          signature = infer_signature(train, predictions)
      :param input_example: (Experimental) Input example provides one or several instances of valid
                            model input. The example can be used as a hint of what data to feed the
                            model. The given example will be converted to a Pandas DataFrame and then
                            serialized to json using the Pandas split-oriented format. Bytes are
                            base64-encoded.
  
      :param requirements_file: A string containing the path to requirements file. Remote URIs
                        are resolved to absolute filesystem paths.
                        For example, consider the following ``requirements_file`` string -
  
                        requirements_file = "s3://my-bucket/path/to/my_file"
  
                        In this case, the ``"my_file"`` requirements file is downloaded from S3.
  
                        If ``None``, no requirements file is added to the model.
  
      :param extra_files: A list containing the paths to corresponding extra files. Remote URIs
                        are resolved to absolute filesystem paths.
                        For example, consider the following ``extra_files`` list -
  
                        extra_files = ["s3://my-bucket/path/to/my_file1",
                                      "s3://my-bucket/path/to/my_file2"]
  
                        In this case, the ``"my_file1 & my_file2"`` extra file is downloaded from S3.
  
                        If ``None``, no extra files are added to the model.
  
      :param kwargs: kwargs to pass to ``torch.save`` method.
  
      .. code-block:: python
          :caption: Example
  
          import os
  
          import torch
          import mlflow.pytorch
  
          # Class defined here
          class LinearNNModel(torch.nn.Module):
              ...
  
          # Initialize our model, criterion and optimizer
          ...
  
          # Training loop
          ...
  
          # Save PyTorch models to current working directory
          with mlflow.start_run() as run:
              mlflow.pytorch.save_model(model, "model")
  
              # Convert to a scripted model and save it
              scripted_pytorch_model = torch.jit.script(model)
              mlflow.pytorch.save_model(scripted_pytorch_model, "scripted_model")
  
          # Load each saved model for inference
          for model_path in ["model", "scripted_model"]:
              model_uri = "{}/{}".format(os.getcwd(), model_path)
              loaded_model = mlflow.pytorch.load_model(model_uri)
              print("Loaded {}:".format(model_path))
              for x in [6.0, 8.0, 12.0, 30.0]:
                  X = torch.Tensor([[x]])
                  y_pred = loaded_model(X)
                  print("predict X: {}, y_pred: {:.2f}".format(x, y_pred.data.item()))
              print("--")
  
      .. code-block:: text
          :caption: Output
  
          Loaded model:
          predict X: 6.0, y_pred: 11.90
          predict X: 8.0, y_pred: 15.92
          predict X: 12.0, y_pred: 23.96
          predict X: 30.0, y_pred: 60.13
          --
          Loaded scripted_model:
          predict X: 6.0, y_pred: 11.90
          predict X: 8.0, y_pred: 15.92
          predict X: 12.0, y_pred: 23.96
          predict X: 30.0, y_pred: 60.13
      """
      import torch
  
      pickle_module = pickle_module or mlflow_pytorch_pickle_module
  
      if not isinstance(pytorch_model, torch.nn.Module):
          raise TypeError("Argument 'pytorch_model' should be a torch.nn.Module")
      if code_paths is not None:
          if not isinstance(code_paths, list):
              raise TypeError("Argument code_paths should be a list, not {}".format(type(code_paths)))
      path = os.path.abspath(path)
      if os.path.exists(path):
          raise RuntimeError("Path '{}' already exists".format(path))
  
      if mlflow_model is None:
          mlflow_model = Model()
  
      os.makedirs(path)
      if signature is not None:
          mlflow_model.signature = signature
      if input_example is not None:
          _save_example(mlflow_model, input_example, path)
  
      model_data_subpath = "data"
      model_data_path = os.path.join(path, model_data_subpath)
      os.makedirs(model_data_path)
      # Persist the pickle module name as a file in the model's `data` directory. This is necessary
      # because the `data` directory is the only available parameter to `_load_pyfunc`, and it
      # does not contain the MLmodel configuration; therefore, it is not sufficient to place
      # the module name in the MLmodel
      #
      # TODO: Stop persisting this information to the filesystem once we have a mechanism for
      # supplying the MLmodel configuration to `mlflow.pytorch._load_pyfunc`
      pickle_module_path = os.path.join(model_data_path, _PICKLE_MODULE_INFO_FILE_NAME)
      with open(pickle_module_path, "w") as f:
          f.write(pickle_module.__name__)
      # Save pytorch model
      model_path = os.path.join(model_data_path, _SERIALIZED_TORCH_MODEL_FILE_NAME)
      if isinstance(pytorch_model, torch.jit.ScriptModule):
          torch.jit.ScriptModule.save(pytorch_model, model_path)
      else:
          torch.save(pytorch_model, model_path, pickle_module=pickle_module, **kwargs)
  
      torchserve_artifacts_config = {}
  
      if requirements_file:
          if not isinstance(requirements_file, str):
              raise TypeError("Path to requirements file should be a string")
  
          with TempDir() as tmp_requirements_dir:
              _download_artifact_from_uri(
                  artifact_uri=requirements_file, output_path=tmp_requirements_dir.path()
              )
              rel_path = os.path.basename(requirements_file)
              torchserve_artifacts_config[_REQUIREMENTS_FILE_KEY] = {"path": rel_path}
              shutil.move(tmp_requirements_dir.path(rel_path), path)
  
      if extra_files:
          torchserve_artifacts_config[_EXTRA_FILES_KEY] = []
          if not isinstance(extra_files, list):
              raise TypeError("Extra files argument should be a list")
  
          with TempDir() as tmp_extra_files_dir:
              for extra_file in extra_files:
                  _download_artifact_from_uri(
                      artifact_uri=extra_file, output_path=tmp_extra_files_dir.path()
                  )
                  rel_path = posixpath.join(_EXTRA_FILES_KEY, os.path.basename(extra_file),)
                  torchserve_artifacts_config[_EXTRA_FILES_KEY].append({"path": rel_path})
              shutil.move(
                  tmp_extra_files_dir.path(), posixpath.join(path, _EXTRA_FILES_KEY),
              )
  
      conda_env_subpath = "conda.yaml"
      if conda_env is None:
          conda_env = get_default_conda_env()
      elif not isinstance(conda_env, dict):
          with open(conda_env, "r") as f:
              conda_env = yaml.safe_load(f)
      with open(os.path.join(path, conda_env_subpath), "w") as f:
          yaml.safe_dump(conda_env, stream=f, default_flow_style=False)
  
      if code_paths is not None:
          code_dir_subpath = "code"
          for code_path in code_paths:
              _copy_file_or_tree(src=code_path, dst=path, dst_dir=code_dir_subpath)
      else:
          code_dir_subpath = None
  
      mlflow_model.add_flavor(
          FLAVOR_NAME,
          model_data=model_data_subpath,
          pytorch_version=torch.__version__,
          **torchserve_artifacts_config,
      )
      pyfunc.add_to_model(
          mlflow_model,
          loader_module="mlflow.pytorch",
          data=model_data_subpath,
          pickle_module_name=pickle_module.__name__,
          code=code_dir_subpath,
          env=conda_env_subpath,
      )
      mlflow_model.save(os.path.join(path, MLMODEL_FILE_NAME))
  
  
  def _load_model(path, **kwargs):
      """
      :param path: The path to a serialized PyTorch model.
      :param kwargs: Additional kwargs to pass to the PyTorch ``torch.load`` function.
      """
      import torch
  
      if os.path.isdir(path):
          # `path` is a directory containing a serialized PyTorch model and a text file containing
          # information about the pickle module that should be used by PyTorch to load it
          model_path = os.path.join(path, "model.pth")
          pickle_module_path = os.path.join(path, _PICKLE_MODULE_INFO_FILE_NAME)
          with open(pickle_module_path, "r") as f:
              pickle_module_name = f.read()
          if "pickle_module" in kwargs and kwargs["pickle_module"].__name__ != pickle_module_name:
              _logger.warning(
                  "Attempting to load the PyTorch model with a pickle module, '%s', that does not"
                  " match the pickle module that was used to save the model: '%s'.",
                  kwargs["pickle_module"].__name__,
                  pickle_module_name,
              )
          else:
              try:
                  kwargs["pickle_module"] = importlib.import_module(pickle_module_name)
              except ImportError as exc:
                  raise MlflowException(
                      message=(
                          "Failed to import the pickle module that was used to save the PyTorch"
                          " model. Pickle module name: `{pickle_module_name}`".format(
                              pickle_module_name=pickle_module_name
                          )
                      ),
                      error_code=RESOURCE_DOES_NOT_EXIST,
                  ) from exc
  
      else:
          model_path = path
  
      if LooseVersion(torch.__version__) >= LooseVersion("1.5.0"):
          return torch.load(model_path, **kwargs)
      else:
          try:
              # load the model as an eager model.
              return torch.load(model_path, **kwargs)
          except Exception:  # pylint: disable=broad-except
              # If fails, assume the model as a scripted model
              return torch.jit.load(model_path)
  
  
  def load_model(model_uri, **kwargs):
      """
      Load a PyTorch model from a local file or a run.
  
      :param 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``
                        - ``models:/<model_name>/<model_version>``
                        - ``models:/<model_name>/<stage>``
  
                        For more information about supported URI schemes, see
                        `Referencing Artifacts <https://www.mlflow.org/docs/latest/concepts.html#
                        artifact-locations>`_.
  
      :param kwargs: kwargs to pass to ``torch.load`` method.
      :return: A PyTorch model.
  
      .. code-block:: python
          :caption: Example
  
          import torch
          import mlflow.pytorch
  
          # Class defined here
          class LinearNNModel(torch.nn.Module):
              ...
  
          # Initialize our model, criterion and optimizer
          ...
  
          # Training loop
          ...
  
          # Log the model
          with mlflow.start_run() as run:
              mlflow.pytorch.log_model(model, "model")
  
          # Inference after loading the logged model
          model_uri = "runs:/{}/model".format(run.info.run_id)
          loaded_model = mlflow.pytorch.load_model(model_uri)
          for x in [4.0, 6.0, 30.0]:
              X = torch.Tensor([[x]])
              y_pred = loaded_model(X)
              print("predict X: {}, y_pred: {:.2f}".format(x, y_pred.data.item()))
  
      .. code-block:: text
          :caption: Output
  
          predict X: 4.0, y_pred: 7.57
          predict X: 6.0, y_pred: 11.64
          predict X: 30.0, y_pred: 60.48
      """
      import torch
  
      local_model_path = _download_artifact_from_uri(artifact_uri=model_uri)
      try:
          pyfunc_conf = _get_flavor_configuration(
              model_path=local_model_path, flavor_name=pyfunc.FLAVOR_NAME
          )
      except MlflowException:
          pyfunc_conf = {}
      code_subpath = pyfunc_conf.get(pyfunc.CODE)
      if code_subpath is not None:
          pyfunc_utils._add_code_to_system_path(
              code_path=os.path.join(local_model_path, code_subpath)
          )
  
      pytorch_conf = _get_flavor_configuration(model_path=local_model_path, flavor_name=FLAVOR_NAME)
      if torch.__version__ != pytorch_conf["pytorch_version"]:
          _logger.warning(
              "Stored model version '%s' does not match installed PyTorch version '%s'",
              pytorch_conf["pytorch_version"],
              torch.__version__,
          )
      torch_model_artifacts_path = os.path.join(local_model_path, pytorch_conf["model_data"])
      return _load_model(path=torch_model_artifacts_path, **kwargs)
  
  
  def _load_pyfunc(path, **kwargs):
      """
      Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``.
  
      :param path: Local filesystem path to the MLflow Model with the ``pytorch`` flavor.
      """
      return _PyTorchWrapper(_load_model(path, **kwargs))
  
  
  class _PyTorchWrapper(object):
      """
      Wrapper class that creates a predict function such that
      predict(data: pd.DataFrame) -> model's output as pd.DataFrame (pandas DataFrame)
      """
  
      def __init__(self, pytorch_model):
          self.pytorch_model = pytorch_model
  
      def predict(self, data, device="cpu"):
          import torch
  
          if not isinstance(data, pd.DataFrame):
              raise TypeError("Input data should be pandas.DataFrame")
          self.pytorch_model.to(device)
          self.pytorch_model.eval()
          with torch.no_grad():
              input_tensor = torch.from_numpy(data.values.astype(np.float32)).to(device)
              preds = self.pytorch_model(input_tensor)
              if not isinstance(preds, torch.Tensor):
                  raise TypeError(
                      "Expected PyTorch model to output a single output tensor, "
                      "but got output of type '{}'".format(type(preds))
                  )
              predicted = pd.DataFrame(preds.numpy())
              predicted.index = data.index
              return predicted
  
  
  @experimental
  @autologging_integration(FLAVOR_NAME)
  def autolog(log_every_n_epoch=1, log_models=True, disable=False):  # pylint: disable=unused-argument
      """
      Enables (or disables) and configures autologging from `PyTorch Lightning
      <https://pytorch-lightning.readthedocs.io/en/latest>`_ to MLflow.
  
      Autologging is performed when you call the `fit` method of
      `pytorch_lightning.Trainer() \
      <https://pytorch-lightning.readthedocs.io/en/latest/trainer.html#>`_.
  
      Explore the complete `PyTorch MNIST \
      <https://github.com/mlflow/mlflow/tree/master/examples/pytorch/MNIST/example1>`_ for
      an expansive example with implementation of additional lightening steps.
  
      **Note**: Autologging is only supported for PyTorch Lightning models,
      i.e., models that subclass
      `pytorch_lightning.LightningModule \
      <https://pytorch-lightning.readthedocs.io/en/latest/lightning_module.html>`_.
      In particular, autologging support for vanilla PyTorch models that only subclass
      `torch.nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_
      is not yet available.
  
      :param log_every_n_epoch: If specified, logs metrics once every `n` epochs. By default, metrics
                         are logged after every epoch.
      :param log_models: If ``True``, trained models are logged as MLflow model artifacts.
                         If ``False``, trained models are not logged.
      :param disable: If ``True``, disables all supported autologging integrations. If ``False``,
                      enables all supported autologging integrations.
  
      .. code-block:: python
          :caption: Example
  
          import os
  
          import pytorch_lightning as pl
          import torch
          from torch.nn import functional as F
          from torch.utils.data import DataLoader
          from torchvision import transforms
          from torchvision.datasets import MNIST
          from pytorch_lightning.metrics.functional import accuracy
  
          import mlflow.pytorch
          from mlflow.tracking import MlflowClient
  
          # For brevity, here is the simplest most minimal example with just a training
          # loop step, (no validation, no testing). It illustrates how you can use MLflow
          # to auto log parameters, metrics, and models.
  
          class MNISTModel(pl.LightningModule):
              def __init__(self):
                  super(MNISTModel, self).__init__()
                  self.l1 = torch.nn.Linear(28 * 28, 10)
  
              def forward(self, x):
                  return torch.relu(self.l1(x.view(x.size(0), -1)))
  
              def training_step(self, batch, batch_nb):
                  x, y = batch
                  loss = F.cross_entropy(self(x), y)
                  acc = accuracy(loss, y)
  
                  # Use the current of PyTorch logger
                  self.log("train_loss", loss, on_epoch=True)
                  self.log("acc", acc, on_epoch=True)
                  return loss
  
              def configure_optimizers(self):
                  return torch.optim.Adam(self.parameters(), lr=0.02)
  
          def print_auto_logged_info(r):
  
              tags = {k: v for k, v in r.data.tags.items() if not k.startswith("mlflow.")}
              artifacts = [f.path for f in MlflowClient().list_artifacts(r.info.run_id, "model")]
              print("run_id: {}".format(r.info.run_id))
              print("artifacts: {}".format(artifacts))
              print("params: {}".format(r.data.params))
              print("metrics: {}".format(r.data.metrics))
              print("tags: {}".format(tags))
  
          # Initialize our model
          mnist_model = MNISTModel()
  
          # Initialize DataLoader from MNIST Dataset
          train_ds = MNIST(os.getcwd(), train=True,
              download=True, transform=transforms.ToTensor())
          train_loader = DataLoader(train_ds, batch_size=32)
  
          # Initialize a trainer
          trainer = pl.Trainer(max_epochs=20, progress_bar_refresh_rate=20)
  
          # Auto log all MLflow entities
          mlflow.pytorch.autolog()
  
          # Train the model
          with mlflow.start_run() as run:
              trainer.fit(mnist_model, train_loader)
  
          # fetch the auto logged parameters and metrics
          print_auto_logged_info(mlflow.get_run(run_id=run.info.run_id))
  
      .. code-block:: text
          :caption: Output
  
          run_id: 42caa17b60cb489c8083900fb52506a7
          artifacts: ['model/MLmodel', 'model/conda.yaml', 'model/data']
          params: {'betas': '(0.9, 0.999)',
                   'weight_decay': '0',
                   'epochs': '20',
                   'eps': '1e-08',
                   'lr': '0.02',
                   'optimizer_name': 'Adam', '
                   amsgrad': 'False'}
          metrics: {'acc_step': 0.0,
                    'train_loss_epoch': 1.0917967557907104,
                    'train_loss_step': 1.0794280767440796,
                    'train_loss': 1.0794280767440796,
                    'acc_epoch': 0.0033333334140479565,
                    'acc': 0.0}
          tags: {'Mode': 'training'}
  
      .. figure:: ../_static/images/pytorch_lightening_autolog.png
  
          PyTorch autologged MLflow entities
      """
      import pytorch_lightning as pl
      from mlflow.pytorch._pytorch_autolog import _create_patch_fit
  
      fit = _create_patch_fit(log_every_n_epoch=log_every_n_epoch, log_models=log_models)
      safe_patch(FLAVOR_NAME, pl.Trainer, "fit", fit, manage_run=True)
  
apis: torch.jit.ScriptModule.save torch.load torch.no_grad torch.save torch.jit.load