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Complete this python program:
def local_elemwise_fusion_op(OP, max_input_fct=(lambda node: 32), maker=None):
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#Here is the Python code you requested.

    """We parametrize it to make it work for Elemwise and GpuElemwise op.
    Parameters
    OP
    GpuElemwise or Elemwise class (the one that we want to fuse)
    max_input_fct
    A function that returns the maximum number of inputs
    that this elemwise can take (useful for GpuElemwise).
    GPU kernel currently has a limit of 256 bytes for
    the size of all parameters passed to it. As currently
    we pass many information only by parameter, we must
    limit how many ops we fuse together to avoid busting
    that 256 limit.
    On the CPU we limit to 32 input variables
    since that is the maximum numpy support.
    """
    if (maker is None):
        def maker(node, scalar_op):
            return OP(scalar_op)
    def local_fuse(node):
        '\n                                As    part    of    specialization,    we    fuse    two    consecutive    elemwise    Ops    of    the\n                                same    shape.\n\n                                For    mixed    dtype,    we    let    the    Composite    op    do    the    cast.    It    lets    the    C\n                                compiler    do    the    cast.\n                                The    number    of    dimensions    is    validated    at    call    time    by    theano    itself.\n\n                                '
        if (type(node.op) is not OP):
            return False
        if (len(node.outputs) > 1):
            return
        inputs = []
        s_inputs = []
        s_g = []
        max_nb_input = max_input_fct(node)
        new_nb_input = len(node.inputs)
        fused = False
        for i in node.inputs:
            do_fusion = False
            catch = False
            tmp_input = []
            tmp_scalar = []
            if (i.owner and isinstance(i.owner.op, OP) and (len(set([n for (n, idx) in i.clients])) == 1) and (i.owner.outputs[0].broadcastable == node.outputs[0].broadcastable)):
                do_fusion = True
                try:
                    tmp_s_input = []
                    for ii in i.owner.inputs:
                        if (ii in inputs):
                            tmp_s_input.append(s_inputs[inputs.index(ii)])
                        elif (ii in tmp_input):
                            tmp_s_input.append(tmp_scalar[tmp_input.index(ii)])
                        else:
                            tmp = scalar.get_scalar_type(ii.dtype).make_variable()
                            try:
                                tv = gof.op.get_test_value(ii)
                                if (tv.size > 0):
                                    tmp.tag.test_value = tv.flatten()[0]
                               ...