class_docstr: Layer with the rectifier (ReLu) used as an activation function.
  It applies linear transformation when the ``n_units`` parameter
  specified and ``relu`` function after the transformation. When
  ``n_units`` is not specified, only ``relu`` function will be applied
  to the input.
  
  Parameters
  ----------
  {Linear.n_units}
  
  alpha : float
      Alpha parameter defines the decreasing rate
      for the negative values. If ``alpha``
      is non-zero value then layer behave like a
      leaky ReLu. Defaults to ``0``.
  
  weight : array-like, Tensorfow variable, scalar or Initializer
      Defines layer's weights. Default initialization methods
      you can find :ref:`here <init-methods>`.
      Defaults to :class:`HeNormal(gain=2) <neupy.init.HeNormal>`.
  
  {Linear.bias}
  
  {BaseLayer.name}
  
  Methods
  -------
  {Linear.Methods}
  
  Attributes
  ----------
  {Linear.Attributes}
  
  Examples
  --------
  Feedforward Neural Networks (FNN)
  
  >>> from neupy.layers import *
  >>> network = Input(10) >> Relu(20) >> Relu(1)
  
  Convolutional Neural Networks (CNN)
  
  >>> from neupy.layers import *
  >>> network = join(
  ...     Input((32, 32, 3)),
  ...     Convolution((3, 3, 16)) >> Relu(),
  ...     Convolution((3, 3, 32)) >> Relu(),
  ...     Reshape(),
  ...     Softmax(10),
  ... )
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code: def __init__(
      self,
      n_units=None,
      alpha=0,
      weight=init.HeNormal(gain=2),
      bias=init.Constant(value=0),
      name=None,
  ):
      self.alpha = alpha
      super(Relu, self).__init__(
          n_units=n_units, weight=weight, bias=bias, name=name
      )
  
imports: import numpy
  import tensorflow
  from neupy import init
  from neupy.utils import asfloat, as_tuple, tf_utils
  from neupy.exceptions import LayerConnectionError, WeightInitializationError
  from neupy.core.properties import NumberProperty, TypedListProperty, ParameterProperty, IntProperty
  from .base import BaseLayer
label_desc: Use image node_id 1 to create a new Relu object from inherited base classes: Linear with example: obj = Relu(n_units, alpha, weight, bias, name)