class_docstr: Abstract base class for deep learning time series classifiers.
  
  The base classifier provides a deep learning default method for
  _predict and _predict_proba, and provides a new abstract method for building a
  model.
  
  Parameters
  ----------
  batch_size : int, default = 40
      training batch size for the model
  
  Attributes
  ----------
  self.model_ - the fitted DL model
docstr: Load object from serialized memory container.
  
  Parameters
  ----------
  serial: 1st element of output of `cls.save(None)`
          This is a tuple of size 3.
          The first element represents pickle-serialized instance.
          The second element represents h5py-serialized `keras` model.
          The third element represent pickle-serialized history of `.fit()`.
  
  Returns
  -------
  Deserialized self resulting in output `serial`, of `cls.save(None)`
docstr_tok: ["Load","object","from","serialized","memory","container",".","Parameters","--","--","--","--","--","serial",":","1st","element","of","output","of","`","cls.save","(","None",")","`","This","is","a","tuple","of","size","3",".","The","first","element","represents","pickle-serialized","instance",".","The","second","element","represents","h5py-serialized","`","keras","`","model",".","The","third","element","represent","pickle-serialized","history","of","`",".fit","(",")","`",".","Returns","--","--","--","-","Deserialized","self","resulting","in","output","`","serial","`",",","of","`","cls.save","(","None",")","`"]
code: def load_from_serial(cls, serial):
      """Load object from serialized memory container.
  
      Parameters
      ----------
      serial: 1st element of output of `cls.save(None)`
              This is a tuple of size 3.
              The first element represents pickle-serialized instance.
              The second element represents h5py-serialized `keras` model.
              The third element represent pickle-serialized history of `.fit()`.
  
      Returns
      -------
      Deserialized self resulting in output `serial`, of `cls.save(None)`
      """
      _check_soft_dependencies("h5py")
      import pickle
      from tempfile import TemporaryFile
  
      import h5py
      from tensorflow.keras.models import load_model
  
      if not isinstance(serial, tuple):
          raise TypeError(
              "`serial` is expected to be a tuple, "
              f"instead found of type: {type(serial)}"
          )
      if len(serial) != 3:
          raise ValueError(
              "`serial` should have 3 elements. "
              "All 3 elements represent in-memory serialization "
              "of the estimator. "
              f"Found a tuple of length: {len(serial)} instead."
          )
  
      serial, in_memory_model, in_memory_history = serial
      if in_memory_model is None:
          cls.model_ = None
      else:
          with TemporaryFile() as store_:
              store_.write(in_memory_model)
              h5file = h5py.File(store_, "r")
              cls.model_ = load_model(h5file)
              h5file.close()
  
      cls.history = pickle.loads(in_memory_history)
      return pickle.loads(serial)
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  from sklearn.utils import check_random_state
  from sktime.base._base import SERIALIZATION_FORMATS
  from sktime.classification.base import BaseClassifier
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 11 for calling the BaseDeepClassifier obj's underlying member method code with example usage: obj.load_from_serial(cls, serial) and returns: pickle