code: def simps(x_gpu, dx=1.0, even="avg", handle=None):
      """
      Implementation of composite Simpson's rule similar to
      scipy.integrate.simps.
  
      Integrate x_gpu with spacing dx using composite Simpson's rule.
      If there are an even number of samples, N, then there are an odd
      number of intervals (N-1), but Simpson's rule requires an even number
      of intervals. The parameter 'even' controls how this is handled.
  
      Parameters
      ----------
      x_gpu : pycuda.gpuarray.GPUArray
          Input array to integrate.
      dx : scalar
          Spacing.
      even : str {'avg', 'first', 'last'}, optional
          'avg' : Average two results:1) use the first N-2 intervals with
                    a trapezoidal rule on the last interval and 2) use the last
                    N-2 intervals with a trapezoidal rule on the first interval.
          'first' : Use Simpson's rule for the first N-2 intervals with
                  a trapezoidal rule on the last interval.
          'last' : Use Simpson's rule for the last N-2 intervals with a
                 trapezoidal rule on the first interval.
      handle : int
          CUBLAS context. If no context is specified, the default handle from
          `skcuda.misc._global_cublas_handle` is used.
  
      Returns
      -------
      result : float
          Definite integral as approximated by the composite Simpson's rule.
  
      Examples
      --------
      >>> import pycuda.autoinit
      >>> import pycuda.gpuarray
      >>> import numpy as np
      >>> import integrate
      >>> integrate.init()
      >>> x_gpu = gpuarray.arange(0,10,dtype=np.float64)
      >>> integrate.simps(x_gpu)
      40.5
      >>> x_gpu**=3
      >>> integrate.simps(x_gpu)
      1642.5
      >>> integrate.simps(x_gpu, even='first')
      1644.5
      """
  
      if handle is None:
          handle = misc._global_cublas_handle
  
      if len(x_gpu.shape) > 1:
          raise ValueError("input array must be 1D")
      if np.iscomplex(dx):
          raise ValueError("dx must be real")
  
      float_type = x_gpu.dtype.type
      if float_type == np.complex64:
          cublas_func = cublas.cublasCdotu
      elif float_type == np.float32:
          cublas_func = cublas.cublasSdot
      elif float_type == np.complex128:
          cublas_func = cublas.cublasZdotu
      elif float_type == np.float64:
          cublas_func = cublas.cublasDdot
      else:
          raise ValueError("unsupported input type")
  
      simps_mult_gpu = gen_simps_mult(x_gpu.size, float_type, even)
      result = cublas_func(
          handle,
          x_gpu.size,
          x_gpu.gpudata,
          1,
          simps_mult_gpu.gpudata,
          1,
      )
  
      return float_type(dx) * result
  
file_path: scikit-cuda/skcuda/integrate.py
imports: from string import Template
  from pycuda.tools import context_dependent_memoize
  from pycuda.compiler import SourceModule
  import pycuda.elementwise
  import pycuda.gpuarray
  import pycuda.tools
  import numpy
  from .misc import init, shutdown
  from .None import cublas
  from .None import misc
label_desc: Use image node_id 4 for calling a global function with example usage: simps(x_gpu, dx, even, handle) and returns: unknown