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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code: def __init__(
      self,
      n_epochs=1500,
      callbacks=None,
      verbose=False,
      loss="mean_squared_error",
      metrics=None,
      batch_size=16,
      random_state=None,
      activation="linear",
      use_bias=True,
      optimizer=None,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__()
  
      self.n_epochs = n_epochs
      self.callbacks = callbacks
      self.verbose = verbose
      self.loss = loss
      self.metrics = metrics
      self.batch_size = batch_size
      self.random_state = random_state
      self.activation = activation
      self.use_bias = use_bias
      self.optimizer = optimizer
      self.history = None
      self._network = ResNetNetwork(random_state=random_state)
  
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 1 to create a new ResNetRegressor object from inherited base classes: BaseDeepRegressor with example: obj = ResNetRegressor(n_epochs, callbacks, verbose, loss, metrics, batch_size, random_state, activation, use_bias, optimizer)