class_docstr: Permutes the dimensions of the input according to a given pattern.
  Useful for connecting RNNs and convnets together.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  dims: Tuple of int. Permutation pattern, does not include the batch dimension.
        Indexing starts at 1.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> permute = Permute((2, 1, 3), input_shape=(3, 4, 5))
  creating: createZooKerasPermute
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code: def __init__(self, dims, input_shape=None, **kwargs):
      super(Permute, self).__init__(
          None,
          dims,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
imports: import sys
  from bigdl.dllib.utils.common import INTMIN
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new Permute object from inherited base classes: ZooKerasLayer with example: obj = Permute(dims, input_shape)