old_contents: from syft.controller import cmd, params_func, no_params_func
  
  def concatenate(tensors,axis=0):
      ids = list()
      for t in tensors:
          ids.append(t.id)
      return params_func(cmd, "concatenate", params=[axis] + ids, return_type='FloatTensor')
  
  def ones(*args):
      """
      Returns a tensor filled with zeros
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "ones", params=dims, return_type='FloatTensor')
  
  def randn(*args):
      """
      Returns a tensor filled with random numbers from a normal distribution with mean=0 and std=1
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "randn", params=dims, return_type='FloatTensor')
  
  def random(*args):
      """
      Returns a tensor filled with random numbers from a uniform distribution on the interval [0,1)
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "random", params=dims, return_type='FloatTensor')
  
  def zeros(*args):
      """
      Returns a tensor filled with zeros
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "zeros", params=dims, return_type='FloatTensor')
new_contents: from syft.controller import cmd, params_func, no_params_func
  
  def concatenate(tensors,axis=0):
      ids = list()
      for t in tensors:
          ids.append(t.id)
      return params_func(cmd, "concatenate", params=[axis] + ids, return_type='FloatTensor')
  
  def ones(*args):
      """
      Returns a tensor filled with zeros
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "ones", params=dims, return_type='FloatTensor')
  
  def randn(*args):
      """
      Returns a tensor filled with random numbers from a normal distribution with mean=0 and std=1
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "randn", params=dims, return_type='FloatTensor')
  
  def random(*args):
      """
      Returns a tensor filled with random numbers from a uniform distribution on the interval [0,1)
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "random", params=dims, return_type='FloatTensor')
  
  def set_seed(seed):
      """
      Sets the seed value for the random number generator to make model testing deterministic. 
      """
      assert (type(seed) == int and seed >= 0)
      return params_func(cmd, "set_seed", params=[seed])
  
  def zeros(*args):
      """
      Returns a tensor filled with zeros
      The shape of the tensor is defined by the varargs sizes.
      ----------
      Returns
      -------
      FloatTensor
      """
      dims = list(args)
      assert type(dims[0]) == int
      return params_func(cmd, "zeros", params=dims, return_type='FloatTensor')
subject: Add set_seed() function to pysyft