class_docstr: Base context class that contains runtime information for any Determined
  workflow that uses the ``tf.keras`` API.
class_docstr_tok: ["Base","context","class","that","contains","runtime","information","for","any","Determined","workflow","that","uses","the","``","tf.keras","``","API","."]
docstr: Configure parameters of ``model.fit()``.  See the `Keras documentation
  <https://keras.io/api/>`__ for the meaning of each parameter.
  
  Note that the output of ``verbose=True`` will be visually different in Determined than with
  Keras, for better rendering in trial logs.
  
  Note that if ``configure_fit()`` is called multiple times, any keyword arguments which are
  not provided in the second call will not overwrite any settings configured by the first
  call.
  
  **Usage Example**
  
  .. code:: python
  
     class MyTFKerasTrial(det.keras.TFKerasTrial):
         def __init__(self, context):
             ...
             self.context.configure_fit(verbose=False, workers=5)
  
             # It is safe to call configure_fit() multiple times.
             self.context.configure_fit(use_multiprocessing=True)
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code: def configure_fit(
      self,
      verbose: Optional[bool] = None,
      class_weight: Any = _arg_not_provided,
      workers: Optional[int] = None,
      use_multiprocessing: Optional[bool] = None,
      max_queue_size: Optional[bool] = None,
      shuffle: Optional[bool] = None,
      validation_steps: Any = _arg_not_provided,
  ) -> None:
      """
      Configure parameters of ``model.fit()``.  See the `Keras documentation
      <https://keras.io/api/>`__ for the meaning of each parameter.
  
      Note that the output of ``verbose=True`` will be visually different in Determined than with
      Keras, for better rendering in trial logs.
  
      Note that if ``configure_fit()`` is called multiple times, any keyword arguments which are
      not provided in the second call will not overwrite any settings configured by the first
      call.
  
      **Usage Example**
  
      .. code:: python
  
         class MyTFKerasTrial(det.keras.TFKerasTrial):
             def __init__(self, context):
                 ...
                 self.context.configure_fit(verbose=False, workers=5)
  
                 # It is safe to call configure_fit() multiple times.
                 self.context.configure_fit(use_multiprocessing=True)
      """
      if verbose is not None:
          self._fit_verbose = verbose
      if not isinstance(class_weight, _ArgNotProvided):
          self._fit_class_weight = class_weight
      if workers is not None:
          self._fit_workers = workers
      if use_multiprocessing is not None:
          self._fit_use_multiprocessing = use_multiprocessing
      if max_queue_size is not None:
          self._fit_max_queue_size = max_queue_size
      if shuffle is not None:
          self._fit_shuffle = shuffle
      if not isinstance(validation_steps, _ArgNotProvided):
          self._fit_validation_steps = validation_steps
  
imports: import inspect
  import logging
  from typing import Any, Callable, Dict, List, NamedTuple, Optional, Union, cast
  import tensorflow
  import determined
  from determined import errors, keras, util
  from determined.common import check
  from determined.horovod import hvd
label_desc: Use image node_id 4 for calling the TFKerasTrialContext obj's underlying member method code with example usage: obj.configure_fit(verbose, class_weight, workers, use_multiprocessing, max_queue_size, shuffle, validation_steps) without return types