class_docstr: Simple recurrent neural network.
  
  Parameters
  ----------
  n_epochs : int, default = 100
      the number of epochs to train the model
  batch_size : int, default = 1
      the number of samples per gradient update.
  units : int, default = 6
      number of units in the network
  callbacks : list of tf.keras.callbacks.Callback objects, default = None
  add_default_callback : bool, default = True
      whether to add default callback
  random_state : int or None, default=0
      Seed for random number generation.
  verbose : boolean, default = False
      whether to output extra information
  loss : string, default="mean_squared_error"
      fit parameter for the keras model
  metrics : list of strings, default=["accuracy"]
      metrics to use in fitting the neural network
  activation : string or a tf callable, default="sigmoid"
      Activation function used in the output layer.
      List of available activation functions: https://keras.io/api/layers/activations/
  use_bias : boolean, default = True
      whether the layer uses a bias vector.
  optimizer : keras.optimizers object, default = RMSprop(lr=0.001)
      specify the optimizer and the learning rate to be used.
  
  References
  ----------
  ..[1] benchmark forecaster in M4 forecasting competition:
  https://github.com/Mcompetitions/M4-methods
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      Reserved values for classifiers:
          "results_comparison" - used for identity testing in some classifiers
              should contain parameter settings comparable to "TSC bakeoff"
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          Reserved values for classifiers:
              "results_comparison" - used for identity testing in some classifiers
                  should contain parameter settings comparable to "TSC bakeoff"
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params1 = {}
      params2 = {
          "n_epochs": 50,
          "batch_size": 2,
          "units": 5,
          "use_bias": False,
      }
      return [params1, params2]
  
file_path: sktime/sktime/classification/deep_learning/rnn.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.rnn import RNNNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the SimpleRNNClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list