parameters: (input, size=3, output=None, mode="reflect",
                     cval=0.0, origin=0)
return_statement: return return_value
docstring: Multi-dimensional uniform filter.
  
      Parameters
      ----------
      %(input)s
      size : int or sequence of ints, optional
          The sizes of the uniform filter are given for each axis as a
          sequence, or as a single number, in which case the size is
          equal for all axes.
      %(output)s
      %(mode)s
      %(cval)s
      %(origin)s
  
      Notes
      -----
      The multi-dimensional filter is implemented as a sequence of
      one-dimensional uniform filters. The intermediate arrays are stored
      in the same data type as the output. Therefore, for output types
      with a limited precision, the results may be imprecise because
      intermediate results may be stored with insufficient precision.
docstring_summary: Multi-dimensional uniform filter.
docstring_tokens: Multi - dimensional uniform filter .
function: def uniform_filter(input, size=3, output=None, mode="reflect",
                     cval=0.0, origin=0):
      """Multi-dimensional uniform filter.
  
      Parameters
      ----------
      %(input)s
      size : int or sequence of ints, optional
          The sizes of the uniform filter are given for each axis as a
          sequence, or as a single number, in which case the size is
          equal for all axes.
      %(output)s
      %(mode)s
      %(cval)s
      %(origin)s
  
      Notes
      -----
      The multi-dimensional filter is implemented as a sequence of
      one-dimensional uniform filters. The intermediate arrays are stored
      in the same data type as the output. Therefore, for output types
      with a limited precision, the results may be imprecise because
      intermediate results may be stored with insufficient precision.
      """
      input = numpy.asarray(input)
      output, return_value = _ni_support._get_output(output, input)
      sizes = _ni_support._normalize_sequence(size, input.ndim)
      origins = _ni_support._normalize_sequence(origin, input.ndim)
      axes = list(range(input.ndim))
      axes = [(axes[ii], sizes[ii], origins[ii])
                             for ii in range(len(axes)) if sizes[ii] > 1]
      if len(axes) > 0:
          for axis, size, origin in axes:
              uniform_filter1d(input, int(size), axis, output, mode,
                               cval, origin)
              input = output
      else:
          output[...] = input[...]
      return return_value
function_tokens: def uniform_filter ( input , size = 3 , output = None , mode = "reflect" , cval = 0.0 , origin = 0 ) : input = numpy . asarray ( input ) output , return_value = _ni_support . _get_output ( output , input ) sizes = _ni_support . _normalize_sequence ( size , input . ndim ) origins = _ni_support . _normalize_sequence ( origin , input . ndim ) axes = list ( range ( input . ndim ) ) axes = [ ( axes [ ii ] , sizes [ ii ] , origins [ ii ] ) for ii in range ( len ( axes ) ) if sizes [ ii ] > 1 ] if len ( axes ) > 0 : for axis , size , origin in axes : uniform_filter1d ( input , int ( size ) , axis , output , mode , cval , origin ) input = output else : output [ ... ] = input [ ... ] return return_value