class_docstr: Predictors load models from checkpoints to perform inference.
  
  .. note::
      The base ``Predictor`` class cannot be instantiated directly. Only one of
      its subclasses can be used.
  
  **How does a Predictor work?**
  
  Predictors expose a ``predict`` method that accepts an input batch of type
  ``DataBatchType`` and outputs predictions of the same type as the input batch.
  
  When the ``predict`` method is called the following occurs:
  
  - The input batch is converted into a pandas DataFrame. Tensor input (like a
    ``np.ndarray``) will be converted into a single column Pandas Dataframe.
  - If there is a :ref:`Preprocessor <preprocessor-ref>` saved in the provided
    :class:`Checkpoint <ray.train.Checkpoint>`, the preprocessor will be used to
    transform the DataFrame.
  - The transformed DataFrame will be passed to the model for inference (via the
    ``predictor._predict_pandas`` method).
  - The predictions will be outputted by ``predict`` in the same type as the
    original input.
  
  **How do I create a new Predictor?**
  
  To implement a new Predictor for your particular framework, you should subclass
  the base ``Predictor`` and implement the following two methods:
  
  1. ``_predict_pandas``: Given a pandas.DataFrame input, return a
     pandas.DataFrame containing predictions.
  2. ``from_checkpoint``: Logic for creating a Predictor from a
     :class:`Checkpoint <ray.train.Checkpoint>`.
  3. Optionally ``_predict_numpy`` for better performance when working with
     tensor data to avoid extra copies from Pandas conversions.
docstr: Create a specific predictor from a checkpoint.
  
  Args:
      checkpoint: Checkpoint to load predictor data from.
      kwargs: Arguments specific to predictor implementations.
  
  Returns:
      Predictor: Predictor object.
docstr_tok: ["Create","a","specific","predictor","from","a","checkpoint",".","Args",":","checkpoint",":","Checkpoint","to","load","predictor","data","from",".","kwargs",":","Arguments","specific","to","predictor","implementations",".","Returns",":","Predictor",":","Predictor","object","."]
code: def from_checkpoint(
      cls, checkpoint: Checkpoint, **kwargs
  ) -> "Predictor":
      """Create a specific predictor from a checkpoint.
  
      Args:
          checkpoint: Checkpoint to load predictor data from.
          kwargs: Arguments specific to predictor implementations.
  
      Returns:
          Predictor: Predictor object.
      """
      raise NotImplementedError
  
imports: import abc
  from typing import Callable, Dict, Optional, Type, Union
  import numpy
  import pandas
  from ray.air.data_batch_type import DataBatchType
  from ray.air.util.data_batch_conversion import BatchFormat, _convert_batch_type_to_numpy, _convert_batch_type_to_pandas
  from ray.data import Preprocessor
  from ray.train import Checkpoint
  from ray.util.annotations import DeveloperAPI, PublicAPI
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.from_checkpoint(cls, checkpoint) without return types