class_docstr: InceptionTime Deep Learning Classifier.
  
  Parameters
  ----------
  n_epochs : int, default=1500
  batch_size : int, default=64
      the number of samples per gradient update
  kernel_size : int, default=40
      specifying the length of the 1D convolution window
  n_filters : int, default=32
  use_residual : boolean, default=True
  use_bottleneck : boolean, default=True
  bottleneck_size : int, default=32
  depth : int, default=6
  callbacks : list of tf.keras.callbacks.Callback objects
  random_state: int, optional, default=None
      random seed for internal random number generator
  verbose: boolean, default=False
      whether to print runtime information
  loss: str, default="categorical_crossentropy"
  metrics: optional
  
  Notes
  -----
  ..[1] Fawaz et. al, InceptionTime: Finding AlexNet for Time Series
  Classification, Data Mining and Knowledge Discovery, 34, 2020
  
  Adapted from the implementation from Fawaz et. al
  https://github.com/hfawaz/InceptionTime/blob/master/classifiers/inception.py
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`.
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": 10,
          "batch_size": 4,
      }
  
      param2 = {
          "n_epochs": 12,
          "batch_size": 6,
      }
      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/inceptiontime.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.inceptiontime import InceptionTimeNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 5 for calling the InceptionTimeClassifier obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: test_params