Remove incomplete conversion function for arange_like.

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -2369,7 +2369,7 @@ def convert_slice(node, **kwargs):
 def convert_zeros_like(node, **kwargs):
     """Map MXNet's zeros_like operator attributes to onnx's ConstantOfShape operator.
     """
-    name, input_nodes, attrs = get_inputs(node, kwargs)
+    name, input_nodes, _ = get_inputs(node, kwargs)
     tensor_value = onnx.helper.make_tensor(name+"_val", onnx.TensorProto.INT32, [1], [0])
     node = onnx.helper.make_node(
         "ConstantOfShape",
@@ -2381,46 +2381,3 @@ def convert_zeros_like(node, **kwargs):
     return [node]
 
 
-@mx_op.register("_contrib_arange_like")
-def convert_arange_like(node, **kwargs):
-    """Map MXNet's arange_like operator attributes to onnx's Range operator.
-    """
-    name, input_nodes, attrs = get_inputs(node, kwargs)
-
-    opset_version = kwargs['opset_version']
-    if opset_version < 11:
-        raise AttributeError("ONNX opset 11 or greater is required to export this operator")
-
-    in_shape = kwargs['in_shape']
-    axis = attrs.get('axis')
-    if axis is None:
-        # output will be same shape as input
-        raise NotImplementedError("arange_like operator without axis attribute not yet implemented.")
-    else:
-        # determine shape of axis
-        axis_dim = in_shape[int(axis)]
-
-    start = attrs.get('start', np.float32(0.))
-    step = attrs.get('step', np.float32(1.0))
-    repeat = int(attrs.get('repeat', 1))
-    if repeat != 1:
-        raise NotImplementedError("arange_like operator with repeat != 1 not yet implemented.")
-
-    nodes = [
-        create_const_scalar_node(name+"_start", start, kwargs),
-        create_const_scalar_node(name+"_limit", np.float32(np.inf), kwargs),
-        create_const_scalar_node(name+"_step", step, kwargs)
-    ]
-
-    node = onnx.helper.make_node(
-        "Range",
-        [name+"_start", name+"_limit", name+"_step"],
-        [name],
-        name=name
-    )
-    return [node]
-
-
-
-
-