class_docstr: Multi-Scale Attention Convolutional Neural Classifier, as described in [1]_.
  
  Parameters
  ----------
  n_epochs : int, optional (default=1500)
      The number of epochs to train the model.
  batch_size : int, optional (default=4)
      The number of sample per gradient update.
  padding : str, optional (default="same")
      The type of padding to be provided in MACNN Blocks. Accepts
      all the string values that keras.layers supports.
  pool_size : int, optional (default=3)
      A single value representing pooling windows which are applied
      between two MACNN Blocks.
  strides : int, optional (default=2)
      A single value representing strides to be taken during the
      pooling operation.
  repeats : int, optional (default=2)
      The number of MACNN Blocks to be stacked.
  filter_sizes : tuple, optional (default=(64, 128, 256))
      The input size of Conv1D layers within each MACNN Block.
  kernel_size : tuple, optional (default=(3, 6, 12))
      The output size of Conv1D layers within each MACNN Block.
  reduction : int, optional (default = 16)
      The factor by which the first dense layer of a MACNN Block will be divided by.
  loss : str, optional (default="categorical_crossentropy")
      The name of the loss function to be used during training,
      should be supported by keras.
  activation : str, optional (default="sigmoid")
      The activation function to apply at the output. It should be
      "software" if response variable has more than two types.
  use_bias : bool, optional (default=True)
      Whether bias should be included in the output layer.
  metrics : None or string, optional (default=None)
      The string which will be used during model compilation. If left as None,
      then "accuracy" is passed to `model.compile()`.
  optimizer: None or keras.optimizers.Optimizer instance, optional (default=None)
      The optimizer that is used for model compiltation. If left as None,
      then `keras.optimizers.Adam(learning_rate=0.0001)` is used.
  callbacks : None or list of keras.callbacks.Callback, optional (default=None)
      The callback(s) to use during training.
  random_state : int, optional (default=0)
      The seed to any random action.
  verbose : bool, optional (default=False)
      Verbosity during model training, making it `True` will
      print model summary, training information etc.
  
  References
  ----------
  .. [1] Wei Chen et. al, Multi-scale Attention Convolutional
  Neural Network for time series classification,
  Neural Networks, Volume 136, 2021, Pages 126-140, ISSN 0893-6080,
  https://doi.org/10.1016/j.neunet.2021.01.001.
  
  Examples
  --------
  >>> from sktime.classification.deep_learning.macnn import MACNNClassifier
  >>> 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")
  >>> macnn = MACNNClassifier(n_epochs=3) # doctest: +SKIP
  >>> macnn.fit(X_train, y_train) # doctest: +SKIP
  MACNNClassifier(...)
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docstr: Fit the classifier on the training set (X, y).
  
  Parameters
  ----------
  X : np.ndarray of shape = (n_instances (n), n_dimensions (d), series_length (m))
      The training input samples.
  y : np.ndarray of shape n
      The training data class labels.
  
  Returns
  -------
  self : object
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code: def _fit(self, X, y):
      """Fit the classifier on the training set (X, y).
  
      Parameters
      ----------
      X : np.ndarray of shape = (n_instances (n), n_dimensions (d), series_length (m))
          The training input samples.
      y : np.ndarray of shape n
          The training data class labels.
  
      Returns
      -------
      self : object
      """
      y_onehot = self.convert_y_to_keras(y)
      X = X.transpose(0, 2, 1)
  
      check_random_state(self.random_state)
      self.input_shape = X.shape[1:]
      self.model_ = self.build_model(self.input_shape, self.n_classes_)
      self.callbacks_ = deepcopy(self.callbacks)
  
      if self.verbose:
          self.model_.summary()
  
      self.history = self.model_.fit(
          X,
          y_onehot,
          batch_size=self.batch_size,
          epochs=self.n_epochs,
          verbose=self.verbose,
          callbacks=self.callbacks_,
      )
  
      return self
  
file_path: sktime/sktime/classification/deep_learning/macnn.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.macnn import MACNNNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 3 for calling the MACNNClassifier obj's underlying member method code with example usage: obj._fit(X, y) and returns: self