class_docstr: Grid+Random Recipe for TCN
docstr: __init__()
  Constructor.
  
  :param num_rand_samples: number of hyper-param configurations sampled randomly
  :param training_iteration: no. of iterations for training (n epochs) in trials
  :param batch_size: grid search candidates for batch size
  :param hidden_size: grid search candidates for hidden size of each layer
  :param levels: the number of layers
  :param kernel_size: the kernel size of each layer
  :param dropout: dropout rate (1 - keep probability)
  :param lr: learning rate
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code: def __init__(
      self,
      num_rand_samples=1,
      training_iteration=40,
      batch_size=[256, 512],
      hidden_size=[32, 48],
      levels=[6, 8],
      kernel_size=[3, 5],
      dropout=[0, 0.1],
      lr=[0.001, 0.003],
  ):
      """
      __init__()
      Constructor.
  
      :param num_rand_samples: number of hyper-param configurations sampled randomly
      :param training_iteration: no. of iterations for training (n epochs) in trials
      :param batch_size: grid search candidates for batch size
      :param hidden_size: grid search candidates for hidden size of each layer
      :param levels: the number of layers
      :param kernel_size: the kernel size of each layer
      :param dropout: dropout rate (1 - keep probability)
      :param lr: learning rate
      """
      super(self.__class__, self).__init__()
      # -- run time params
      self.num_samples = num_rand_samples
      self.training_iteration = training_iteration
  
      # -- optimization params
      self.lr = hp.choice(lr)
      self.batch_size = hp.grid_search(batch_size)
  
      # ---- model params
      self.hidden_size = hp.grid_search(hidden_size)
      self.levels = hp.grid_search(levels)
      self.kernel_size = hp.grid_search(kernel_size)
      self.dropout = hp.choice(dropout)
  
imports: from zoo.chronos.autots.deprecated.config.base import Recipe
  from zoo.orca.automl import hp
  from zoo.chronos.utils import deprecated
label_desc: Use image node_id 1 to create a new TCNGridRandomRecipe object from inherited base classes: Recipe with example: obj = TCNGridRandomRecipe(num_rand_samples, training_iteration, batch_size, hidden_size, levels, kernel_size, dropout, lr)