class_docstr: Time series attentional prototype network (TapNet), as described in [1]_.
  
  Parameters
  ----------
  filter_sizes        : array of int, default = (256, 256, 128)
      sets the kernel size argument for each convolutional block.
      Controls number of convolutional filters
      and number of neurons in attention dense layers.
  kernel_sizes        : array of int, default = (8, 5, 3)
      controls the size of the convolutional kernels
  layers              : array of int, default = (500, 300)
      size of dense layers
  reduction           : int, default = 16
      divides the number of dense neurons in the first layer of the attention block.
  n_epochs            : int, default = 2000
      number of epochs to train the model
  batch_size          : int, default = 16
      number of samples per update
  dropout             : float, default = 0.5
      dropout rate, in the range [0, 1)
  dilation            : int, default = 1
      dilation value
  activation          : str, default = "sigmoid"
      activation function for the last output layer
  loss                : str, default = "binary_crossentropy"
      loss function for the classifier
  optimizer           : str or None, default = "Adam(lr=0.01)"
      gradient updating function for the classifier
  use_bias            : bool, default = True
      whether to use bias in the output dense layer
  use_rp              : bool, default = True
      whether to use random projections
  use_att             : bool, default = True
      whether to use self attention
  use_lstm        : bool, default = True
      whether to use an LSTM layer
  use_cnn         : bool, default = True
      whether to use a CNN layer
  verbose         : bool, default = False
      whether to output extra information
  random_state    : int or None, default = None
      seed for random
  
  Attributes
  ----------
  n_classes      : int
      number of classes extracted from the data
  
  References
  ----------
  .. [1] Zhang et al. Tapnet: Multivariate time series classification with
  attentional prototypical network,
  Proceedings of the AAAI Conference on Artificial Intelligence
  34(4), 6845-6852, 2020
  
  Examples
  --------
  >>> from sktime.classification.deep_learning.tapnet import TapNetClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> X_test, y_test = load_unit_test(split="test")
  >>> tapnet = TapNetClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
  >>> tapnet.fit(X_train, y_train) # doctest: +SKIP
  TapNetClassifier(...)
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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": 20,
          "batch_size": 4,
          "use_lstm": False,
          "use_att": False,
          "filter_sizes": (16, 16, 16),
          "dilation": 2,
          "layers": (32, 16),
      }
      param2 = {
          "n_epochs": 20,
          "use_cnn": False,
          "layers": (25, 25),
      }
      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/classification/deep_learning/tapnet.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.tapnet import TapNetNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 4 for calling the TapNetClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: test_params