class_docstr: Establish the network structure for a MLP.
  
  Adapted from the implementation used in [1]
  
  Parameters
  ----------
  random_state    : int, default = 0
      seed to any needed random actions
  
  Notes
  -----
  Adapted from the implementation from source code
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
  
  References
  ----------
  .. [1]  Network originally defined in:
  @inproceedings{wang2017time, title={Time series classification from
  scratch with deep neural networks: A strong baseline}, author={Wang,
  Zhiguang and Yan, Weizhong and Oates, Tim}, booktitle={2017
  International joint conference on neural networks (IJCNN)}, pages={
  1578--1585}, year={2017}, organization={IEEE} }
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code: def __init__(
      self,
      random_state=0,
  ):
      _check_dl_dependencies(severity="error")
      self.random_state = random_state
      super().__init__()
  
imports: from sktime.networks.base import BaseDeepNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 1 to create a new MLPNetwork object from inherited base classes: BaseDeepNetwork with example: obj = MLPNetwork(random_state)