class_docstr: Reduce the dim of a temporal tensor, e.g. output of RNN, to a vector.
  
  # Arguments
      reduction_type: String. 'flatten', 'global_max' or 'global_avg'. If left
          unspecified, it will be tuned automatically.
class_docstr_tok: ["Reduce","the","dim","of","a","temporal","tensor",",","e.g",".","output","of","RNN",",","to","a","vector",".","#","Arguments","reduction_type",":","String",".","'flatten","'",",","'global_max","'","or","'global_avg","'",".","If","left","unspecified",",","it","will","be","tuned","automatically","."]
code: def __init__(self, reduction_type: Optional[str] = None, **kwargs):
      super().__init__(reduction_type, **kwargs)
  
imports: from typing import Optional
  import tensorflow
  from tensorflow import nest
  from tensorflow.keras import layers
  from autokeras.engine import block
  from autokeras.utils import layer_utils
  from autokeras.utils import utils
label_desc: Use image node_id 1 to create a new TemporalReduction object from inherited base classes: Reduction with example: obj = TemporalReduction(reduction_type)