class_docstr: Layer transposes input tensor. Permutes the dimensions according
  to the ``perm`` parameter.
  
  Parameters
  ----------
  perm : tuple or list
      A permutation of the dimensions of the input tensor.
  
  {BaseLayer.name}
  
  Methods
  -------
  {BaseLayer.Methods}
  
  Attributes
  ----------
  {BaseLayer.Attributes}
  
  Examples
  --------
  >>> from neupy.layers import *
  >>> network = Input((7, 11)) >> Transpose((0, 2, 1))
  (?, 7, 11) -> [... 2 layers ...] -> (?, 11, 7)
class_docstr_tok: ["Layer","transposes","input","tensor",".","Permutes","the","dimensions","according","to","the","``","perm","``","parameter",".","Parameters","--","--","--","--","--","perm",":","tuple","or","list","A","permutation","of","the","dimensions","of","the","input","tensor",".","{","BaseLayer.name","}","Methods","--","--","--","-","{","BaseLayer.Methods","}","Attributes","--","--","--","--","--","{","BaseLayer.Attributes","}","Examples","--","--","--","--",">",">",">","from","neupy.layers","import","*",">",">",">","network","=","Input","(","(","7",",","11",")",")",">",">","Transpose","(","(","0",",","2",",","1",")",")","(","?",",","7",",","11",")","-",">","[","...","2","layers","...","]","-",">","(","?",",","11",",","7",")"]
code: def get_output_shape(self, input_shape):
      input_shape = tf.TensorShape(input_shape)
      self.fail_if_shape_invalid(input_shape)
  
      if input_shape.ndims is None:
          n_dims_expected = len(self.perm)
          return tf.TensorShape([None] * n_dims_expected)
  
      input_shape = np.array(input_shape.dims)
      perm = list(self.perm)
  
      return tf.TensorShape(input_shape[perm])
  
file_path: neupy/neupy/layers/reshape.py
imports: import numpy
  import tensorflow
  from neupy.utils import as_tuple
  from neupy.exceptions import LayerConnectionError
  from neupy.core.properties import TypedListProperty
  from .base import BaseLayer
label_desc: Use image node_id 3 for calling the Transpose obj's underlying member method code with example usage: obj.get_output_shape(input_shape) and returns: tf, tf