parameters: (inputs, axis=1, num_output=1, **kwargs)
return_statement: return outputs
docstring: Slice interface of NDArray.
  
      The dimension of specific axis should be divided by ``num_output``.
  
      Parameters
      ----------
      inputs : Tensor
          The input tensor.
      axis : int
          The axis to slice.
      num_output : int
          The number of slices.
  
      Returns
      -------
      Tensor or list of Tensor
          The outputs.
docstring_summary: Slice interface of NDArray.
docstring_tokens: Slice interface of NDArray .
function: def Slice(inputs, axis=1, num_output=1, **kwargs):
      """Slice interface of NDArray.
  
      The dimension of specific axis should be divided by ``num_output``.
  
      Parameters
      ----------
      inputs : Tensor
          The input tensor.
      axis : int
          The axis to slice.
      num_output : int
          The number of slices.
  
      Returns
      -------
      Tensor or list of Tensor
          The outputs.
  
      """
      CheckInputs(inputs, 1)
      arguments = ParseArguments(locals())
  
      outputs = Tensor.CreateOperator(op_type='Slice', nout=num_output, **arguments)
  
      if inputs.shape is not None:
          if inputs.shape[axis] is not None:
              for i in xrange(len(outputs)):
                  outputs[i].shape = inputs.shape[:]
                  outputs[i].shape[axis] /= num_output
  
      return outputs
function_tokens: def Slice ( inputs , axis = 1 , num_output = 1 , * * kwargs ) : CheckInputs ( inputs , 1 ) arguments = ParseArguments ( locals ( ) ) outputs = Tensor . CreateOperator ( op_type = 'Slice' , nout = num_output , * * arguments ) if inputs . shape is not None : if inputs . shape [ axis ] is not None : for i in xrange ( len ( outputs ) ) : outputs [ i ] . shape = inputs . shape [ : ] outputs [ i ] . shape [ axis ] /= num_output return outputs