old_contents: from __future__ import absolute_import
  import theano.tensor as T
  
  
  def softmax(x):
      return T.nnet.softmax(x.reshape((-1, x.shape[-1]))).reshape(x.shape)
  
  
  def time_distributed_softmax(x):
      import warnings
      warnings.warn("time_distributed_softmax is deprecated. Just use softmax!", DeprecationWarning)
      return softmax(x)
  
  
  def softplus(x):
      return T.nnet.softplus(x)
  
  
  def relu(x):
      return T.nnet.relu(X)
  
  
  def tanh(x):
      return T.tanh(x)
  
  
  def sigmoid(x):
      return T.nnet.sigmoid(x)
  
  
  def hard_sigmoid(x):
      return T.nnet.hard_sigmoid(x)
  
  
  def linear(x):
      '''
      The function returns the variable that is passed in, so all types work
      '''
      return x
  
  
  from .utils.generic_utils import get_from_module
  def get(identifier):
      return get_from_module(identifier, globals(), 'activation function')
  
new_contents: from __future__ import absolute_import
  import theano.tensor as T
  
  
  def softmax(x):
      return T.nnet.softmax(x.reshape((-1, x.shape[-1]))).reshape(x.shape)
  
  
  def time_distributed_softmax(x):
      import warnings
      warnings.warn("time_distributed_softmax is deprecated. Just use softmax!", DeprecationWarning)
      return softmax(x)
  
  
  def softplus(x):
      return T.nnet.softplus(x)
  
  
  def relu(x):
      return T.nnet.relu(x)
  
  
  def tanh(x):
      return T.tanh(x)
  
  
  def sigmoid(x):
      return T.nnet.sigmoid(x)
  
  
  def hard_sigmoid(x):
      return T.nnet.hard_sigmoid(x)
  
  
  def linear(x):
      '''
      The function returns the variable that is passed in, so all types work
      '''
      return x
  
  
  from .utils.generic_utils import get_from_module
  def get(identifier):
      return get_from_module(identifier, globals(), 'activation function')
  
subject: Fix relu