code: def make_node(self, inp, s=None):
      # A shape parameter s can be provided as an input. For now this is used to
      # manage odd transform sizes.
      # Later this could be extended to handle padding and trunkation,
      # following numpy's interface. However, cuFFT expects array that match
      # the shape given to the plan, so padding will have to be done in the op.
      # The effect of padding on gradients has yet to be investigated.
  
      if not skcuda_available:
          raise RuntimeError("skcuda is needed for CuFFTOp")
  
      if not pygpu_available:
          raise RuntimeError("pygpu is needed for CuFFTOp")
  
      if not pycuda_available:
          raise RuntimeError("pycuda is needed for CuFFTOp")
  
      inp = gpu_contiguous(
          as_gpuarray_variable(inp, infer_context_name(inp))
      )
  
      # If no shape is provided as input, default to input data shape.
      if s is None:
          s = inp.shape[1:]
      s = T.as_tensor_variable(s)
  
      assert inp.dtype == "float32"
      assert s.ndim == 1
      assert s.dtype in theano.tensor.integer_dtypes
  
      return theano.Apply(self, [inp, s], [self.output_type(inp)()])
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  import theano
  from theano import Op
  import theano.tensor
  from theano.gradient import DisconnectedType
  from .basic_ops import gpu_contiguous, as_gpuarray_variable, infer_context_name
  from .type import GpuArrayType
  import theano.tensor.fft
  from .opt import register_opt, op_lifter, register_opt2
label_desc: Use image node_id 2 for calling the CuRFFTOp obj's underlying member method code with example usage: obj.make_node(inp, s) and returns: theano