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
code: def _check_callbacks(self, callbacks):
      from tensorflow import keras
  
      # if user hasn't provided a custom ReduceLROnPlateau via init already,
      # add the default from literature
      if callbacks is None:
          callbacks = []
  
      if not any(
          isinstance(callback, keras.callbacks.ReduceLROnPlateau)
          for callback in callbacks
      ):
          reduce_lr = keras.callbacks.ReduceLROnPlateau(
              monitor="loss", factor=0.5, patience=50, min_lr=0.0001
          )
          callbacks = callbacks + [reduce_lr]
      return callbacks
  
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 4 for calling the InceptionTimeClassifier obj's underlying member method code with example usage: obj._check_callbacks(callbacks) and returns: callbacks