class_docstr: Layer reshapes input tensor.
  
  Parameters
  ----------
  shape : tuple
      New feature shape. If one dimension specified with the ``-1`` value
      that this dimension will be computed from the total size that remains.
      Defaults to ``-1``.
  
  {BaseLayer.name}
  
  Methods
  -------
  {BaseLayer.Methods}
  
  Attributes
  ----------
  {BaseLayer.Attributes}
  
  Examples
  --------
  
  Covert 4D input to 2D
  
  >>> from neupy.layers import *
  >>> network = Input((2, 5, 5)) >> Reshape()
  (?, 2, 5, 5) -> [... 2 layers ...] -> (?, 50)
  
  Convert 3D to 4D
  
  >>> from neupy.layers import *
  >>> network = Input((5, 4)) >> Reshape((5, 2, 2))
  (?, 5, 4) -> [... 2 layers ...] -> (?, 5, 2, 2)
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code: def __init__(self, shape=-1, name=None):
      super(Reshape, self).__init__(name=name)
      self.shape = as_tuple(shape)
  
      if self.shape.count(-1) >= 2:
          raise ValueError("Only single -1 value can be specified")
  
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 1 to create a new Reshape object from inherited base classes: BaseLayer with example: obj = Reshape(shape, name)