class_docstr: Residual Neural Network Regressor adopted from [1].
  
  Parameters
  ----------
  n_epochs       : int, default = 1500
      the number of epochs to train the model
  batch_size      : int, default = 16
      the number of samples per gradient update.
  random_state    : int or None, default=None
      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
  optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
  metrics         : list of strings, default=["mean_squared_error"],
  activation      : string or a tf callable, default="linear"
      Activation function used in the output linear 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 = Adam(lr=0.01)
      specify the optimizer and the learning rate to be used.
  
  
  Notes
  -----
  Adapted from the implementation from source code
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py
  
  References
  ----------
      .. [1] Wang et. al, Time series classification from
  scratch with deep neural networks: A strong baseline,
  International joint conference on neural networks (IJCNN), 2017.
  
  Examples
  --------
  >>> from sktime.regression.deep_learning.resnet import ResNetRegressor
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> clf = ResNetRegressor(n_epochs=20, batch_size=4) # doctest: +SKIP
  >>> clf.fit(X_train, Y_train) # doctest: +SKIP
  ResNetRegressor(...)
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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.
      For classifiers, a "default" set of parameters should be provided for
      general testing, and a "results_comparison" set for comparing against
      previously recorded results if the general set does not produce suitable
      probabilities to compare against.
  
  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`.
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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.
          For classifiers, a "default" set of parameters should be provided for
          general testing, and a "results_comparison" set for comparing against
          previously recorded results if the general set does not produce suitable
          probabilities to compare against.
  
      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`.
      """
      from sktime.utils.validation._dependencies import (
          _check_soft_dependencies,
      )
  
      param1 = {
          "n_epochs": 6,
          "batch_size": 4,
          "use_bias": False,
      }
  
      param2 = {
          "n_epochs": 4,
          "batch_size": 6,
          "use_bias": True,
      }
      test_params = [param1, param2]
  
      if _check_soft_dependencies("keras", severity="none"):
          from keras.callbacks import LambdaCallback
  
          test_params.append(
              {
                  "n_epochs": 2,
                  "callbacks": [LambdaCallback()],
              }
          )
  
      return test_params
  
file_path: sktime/sktime/regression/deep_learning/resnet.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.networks.resnet import ResNetNetwork
  from sktime.regression.deep_learning.base import BaseDeepRegressor
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 4 for calling the ResNetRegressor obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: test_params