parameters: (a, v, mode='full', propagate_mask=True)
return_statement: return _convolve_or_correlate(np.convolve, a, v, mode, propagate_mask)
docstring: Returns the discrete, linear convolution of two one-dimensional sequences.
  
      Parameters
      ----------
      a, v : array_like
          Input sequences.
      mode : {'valid', 'same', 'full'}, optional
          Refer to the `np.convolve` docstring.
      propagate_mask : bool
          If True, then if any masked element is included in the sum for a result
          element, then the result is masked.
          If False, then the result element is only masked if no non-masked cells
          contribute towards it
  
      Returns
      -------
      out : MaskedArray
          Discrete, linear convolution of `a` and `v`.
  
      See Also
      --------
      numpy.convolve : Equivalent function in the top-level NumPy module.
docstring_summary: Returns the discrete, linear convolution of two one-dimensional sequences.
docstring_tokens: Returns the discrete linear convolution of two one - dimensional sequences .
function: def convolve(a, v, mode='full', propagate_mask=True):
      """
      Returns the discrete, linear convolution of two one-dimensional sequences.
  
      Parameters
      ----------
      a, v : array_like
          Input sequences.
      mode : {'valid', 'same', 'full'}, optional
          Refer to the `np.convolve` docstring.
      propagate_mask : bool
          If True, then if any masked element is included in the sum for a result
          element, then the result is masked.
          If False, then the result element is only masked if no non-masked cells
          contribute towards it
  
      Returns
      -------
      out : MaskedArray
          Discrete, linear convolution of `a` and `v`.
  
      See Also
      --------
      numpy.convolve : Equivalent function in the top-level NumPy module.
      """
      return _convolve_or_correlate(np.convolve, a, v, mode, propagate_mask)
function_tokens: def convolve ( a , v , mode = 'full' , propagate_mask = True ) : return _convolve_or_correlate ( np . convolve , a , v , mode , propagate_mask )