diff --git a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
index 7cf856c767fa..bed316b694c3 100644
--- a/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
+++ b/python/mxnet/contrib/onnx/mx2onnx/_op_translations.py
@@ -74,7 +74,7 @@ def import_onnx_modules():
 def parse_helper(attrs, attrs_name, alt_value=None):
     """Helper function to parse operator attributes in required format."""
     tuple_re = re.compile('\([0-9L|,| ]+\)')
-    if attrs is None:
+    if attrs is None or attrs == {}:
         return alt_value
     attrs_str = None if attrs.get(attrs_name) is None else str(attrs.get(attrs_name))
     if attrs_str is None:
@@ -135,12 +135,21 @@ def get_boolean_attribute_value(attrs, attr_name):
     """
     return 1 if attrs.get(attr_name, 0) in ["True", "1"] else 0
 
+def get_inputs(node, kwargs):
+    """Helper function to get inputs"""
+    onnx = import_onnx_modules()
+    name = node["name"]
+    proc_nodes = kwargs["proc_nodes"]
+    inputs = node["inputs"]
+    attrs = node.get("attrs", {})
+
+    return onnx, name, proc_nodes, inputs, attrs
+
 @mx_op.register("null")
 def convert_weights_and_inputs(node, **kwargs):
     """Helper function to convert weights and inputs.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
+    onnx, name, _, _, _ = get_inputs(node, kwargs)
 
     if kwargs["is_input"] is False:
         weights = kwargs["weights"]
@@ -172,21 +181,16 @@ def convert_convolution(node, **kwargs):
     """Map MXNet's convolution operator attributes to onnx's Conv operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     num_inputs = len(inputs)
 
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[kwargs["index_lookup"][inputs[0][0]]].name
     weights_node = proc_nodes[kwargs["index_lookup"][inputs[1][0]]].name
 
     if num_inputs > 2:
         bias_node = proc_nodes[kwargs["index_lookup"][inputs[2][0]]].name
 
-    attrs = node.get("attrs")
-
     kernel_dims = list(parse_helper(attrs, "kernel"))
     stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
     pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
@@ -219,18 +223,18 @@ def convert_fully_connected(node, **kwargs):
     """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     initializer = kwargs["initializer"]
 
     no_bias = get_boolean_attribute_value(attrs, "no_bias")
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     weight_node_id = kwargs["index_lookup"][inputs[1][0]]
-
-    proc_nodes = kwargs["proc_nodes"]
+    bias_node_id = kwargs["index_lookup"][inputs[2][0]]
+    input_node = proc_nodes[input_node_id]
+    weights_node = proc_nodes[weight_node_id]
+    bias_node = proc_nodes[bias_node_id]
 
     input_node = proc_nodes[input_node_id]
     input_name = input_node.name
@@ -280,12 +284,8 @@ def convert_batchnorm(node, **kwargs):
     """Map MXNet's BatchNorm operator attributes to onnx's BatchNormalization operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    attrs = node["attrs"]
     momentum = float(node.get("attrs", {}).get("momentum", 0.9))
     eps = float(attrs.get("eps", 0.001))
 
@@ -327,11 +327,9 @@ def convert_tanh(node, **kwargs):
     """Map MXNet's tanh operator attributes to onnx's Tanh operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -347,11 +345,9 @@ def convert_cos(node, **kwargs):
     """Map MXNet's cos operator attributes to onnx's Cos operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -367,11 +363,9 @@ def convert_sin(node, **kwargs):
     """Map MXNet's sin operator attributes to onnx's Sin operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -387,11 +381,9 @@ def convert_tan(node, **kwargs):
     """Map MXNet's tan operator attributes to onnx's tan operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -407,11 +399,9 @@ def convert_acos(node, **kwargs):
     """Map MXNet's acos operator attributes to onnx's acos operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -427,11 +417,9 @@ def convert_asin(node, **kwargs):
     """Map MXNet's asin operator attributes to onnx's asin operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -447,11 +435,9 @@ def convert_atan(node, **kwargs):
     """Map MXNet's atan operator attributes to onnx's atan operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -468,11 +454,9 @@ def convert_sigmoid(node, **kwargs):
     """Map MXNet's sigmoid operator attributes to onnx's Sigmoid operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -488,11 +472,9 @@ def convert_relu(node, **kwargs):
     """Map MXNet's relu operator attributes to onnx's Relu operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_node_idx].name
 
     node = onnx.helper.make_node(
@@ -509,14 +491,10 @@ def convert_activation(node, **kwargs):
     """Map MXNet's Activation operator attributes to onnx's Tanh/Relu operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    proc_nodes = kwargs["proc_nodes"]
-    attrs = node["attrs"]
     act_type = attrs["act_type"]
 
-    inputs = node["inputs"]
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_idx].output[0]
 
@@ -551,11 +529,8 @@ def convert_pad(node, **kwargs):
     """Map MXNet's pad operator attributes to onnx's Pad operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    attrs = node["attrs"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_idx].name
 
@@ -608,17 +583,13 @@ def convert_dot(node, **kwargs):
     """Map MXNet's dot operator attributes to onnx's
     MatMul and Transpose operators based on the values set for
     transpose_a, transpose_b attributes."""
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
-    name = node["name"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    input_a_idx = kwargs["index_lookup"][node_inputs[0][0]]
+    input_a_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node_a = proc_nodes[input_a_idx].name
-    input_b_idx = kwargs["index_lookup"][node_inputs[1][0]]
+    input_b_idx = kwargs["index_lookup"][inputs[1][0]]
     input_node_b = proc_nodes[input_b_idx].name
 
-    attrs = node.get('attrs', {})
     trans_a_node = None
     trans_b_node = None
 
@@ -660,26 +631,17 @@ def convert_linalg_gemm2(node, **kwargs):
     transpose_a, transpose_b attributes.
     Return multiple nodes created.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
-    name = node["name"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    input_a_idx = kwargs["index_lookup"][node_inputs[0][0]]
+    input_a_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node_a = proc_nodes[input_a_idx].name
-    input_b_idx = kwargs["index_lookup"][node_inputs[1][0]]
+    input_b_idx = kwargs["index_lookup"][inputs[1][0]]
     input_node_b = proc_nodes[input_b_idx].name
 
     # Getting the attributes and assigning default values.
-    if "attrs" in node:
-        attrs = node["attrs"]
-        alpha = float(attrs["alpha"])
-        trans_a = int(attrs["transpose_a"])
-        trans_b = int(attrs["transpose_b"])
-    else:
-        alpha = 1.0
-        trans_a = 0
-        trans_b = 0
+    alpha = float(attrs.get("alpha", 1.0))
+    trans_a = get_boolean_attribute_value(attrs, "transpose_a")
+    trans_b = get_boolean_attribute_value(attrs, "transpose_b")
 
     op_name = "transpose" + str(kwargs["idx"])
 
@@ -759,18 +721,15 @@ def convert_pooling(node, **kwargs):
     MaxPool/AveragePool/GlobalMaxPool/GlobalAveragePool operators
     based on the input node's attributes and return the created node.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     kernel = eval(attrs["kernel"])
     pool_type = attrs["pool_type"]
     stride = eval(attrs["stride"]) if attrs.get("stride") else None
     global_pool = get_boolean_attribute_value(attrs, "global_pool")
 
-    node_inputs = node["inputs"]
-    input_node_idx = kwargs["index_lookup"][node_inputs[0][0]]
+    input_node_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_idx]
-    name = node["name"]
 
     pooling_convention = attrs.get('pooling_convention', 'valid')
 
@@ -812,10 +771,7 @@ def convert_exp(node, **kwargs):
     """Map MXNet's exp operator attributes to onnx's Exp operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -834,10 +790,7 @@ def convert_identity(node, **kwargs):
     """Map MXNet's _copy operator attributes to onnx's Identity operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -856,13 +809,10 @@ def convert_leakyrelu(node, **kwargs):
     """Map MXNet's LeakyReLU operator attributes to onnx's Elu/LeakyRelu/PRelu operators
     based on the input node's attributes and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
-    attrs = node["attrs"]
 
     act_type = attrs.get("act_type", "leaky")
     alpha = float(attrs.get("slope", 0.25))
@@ -901,14 +851,12 @@ def convert_softmax(node, **kwargs):
     """Map MXNet's softmax operator attributes to onnx's Softmax operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_idx]
 
-    name = node["name"]
-    axis = int(node.get("attrs", {}).get("axis", -1))
+    axis = int(attrs.get("axis", -1))
 
     softmax_node = onnx.helper.make_node(
         "Softmax",
@@ -928,12 +876,10 @@ def convert_softmax_output(node, **kwargs):
     """Map MXNet's SoftmaxOutput operator attributes to onnx's Softmax operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
+
     input1_idx = kwargs["index_lookup"][inputs[0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input1 = proc_nodes[input1_idx]
-    name = node["name"]
 
     softmax_node = onnx.helper.make_node(
         "Softmax",
@@ -951,12 +897,11 @@ def convert_concat(node, **kwargs):
     """Map MXNet's Concat operator attributes to onnx's Concat operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    inputs = node["inputs"]
-    proc_nodes = kwargs["proc_nodes"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_names = [proc_nodes[kwargs["index_lookup"][i[0]]].name for i in inputs]
-    axis = int(node.get("attrs", {}).get("dim", 1))
+
+    axis = int(attrs.get("dim", 1))
     concat_node = onnx.helper.make_node(
         "Concat",
         input_names,
@@ -972,12 +917,12 @@ def convert_transpose(node, **kwargs):
     """Map MXNet's transpose operator attributes to onnx's Transpose operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
     input_idx = kwargs["index_lookup"][node["inputs"][0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_idx].name
-    axes = node.get("attrs", {}).get("axes", ())
+
+    axes = attrs.get("axes", ())
     if axes:
         axes = tuple(map(int, re.findall(r'\d+', axes)))
 
@@ -1004,13 +949,11 @@ def convert_lrn(node, **kwargs):
     """Map MXNet's LRN operator attributes to onnx's LRN operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
+    onnx, name, proc_nodes, _, attrs = get_inputs(node, kwargs)
+
     input_idx = kwargs["index_lookup"][node["inputs"][0][0]]
-    proc_nodes = kwargs["proc_nodes"]
     input_node = proc_nodes[input_idx].name
 
-    attrs = node["attrs"]
     alpha = float(attrs["alpha"]) if "alpha" in attrs else 0.0001
     beta = float(attrs["beta"]) if "beta" in attrs else 0.75
     bias = float(attrs["knorm"]) if "knorm" in attrs else 1.0
@@ -1035,11 +978,10 @@ def convert_l2normalization(node, **kwargs):
     """Map MXNet's L2Normalization operator attributes to onnx's LpNormalization operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    input_id = kwargs["index_lookup"][node["inputs"][0][0]]
-    input_name = kwargs["proc_nodes"][input_id].name
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    input_id = kwargs["index_lookup"][inputs[0][0]]
+    input_name = proc_nodes[input_id].name
     mode = attrs.get("mode", "instance")
 
     if mode != "channel":
@@ -1060,11 +1002,10 @@ def convert_dropout(node, **kwargs):
     """Map MXNet's Dropout operator attributes to onnx's Dropout operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    input_id = kwargs["index_lookup"][node["inputs"][0][0]]
-    input_name = kwargs["proc_nodes"][input_id].name
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    input_id = kwargs["index_lookup"][inputs[0][0]]
+    input_name = proc_nodes[input_id].name
     probability = float(attrs["p"])
 
     dropout_node = onnx.helper.make_node(
@@ -1082,10 +1023,9 @@ def convert_flatten(node, **kwargs):
     """Map MXNet's Flatten operator attributes to onnx's Flatten operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    input_idx = kwargs["index_lookup"][node["inputs"][0][0]]
-    proc_nodes = kwargs["proc_nodes"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
+
+    input_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_idx].name  # .output[0]
 
     flatten_node = onnx.helper.make_node(
@@ -1101,12 +1041,10 @@ def convert_clip(node, **kwargs):
     """Map MXNet's Clip operator attributes to onnx's Clip operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    input_idx = kwargs["index_lookup"][node["inputs"][0][0]]
-    proc_nodes = kwargs["proc_nodes"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    input_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_idx].name
-    attrs = node["attrs"]
     a_min = np.float(attrs.get('a_min', -np.inf))
     a_max = np.float(attrs.get('a_max', np.inf))
 
@@ -1123,11 +1061,9 @@ def convert_clip(node, **kwargs):
 
 def scalar_op_helper(node, op_name, **kwargs):
     """Helper function for scalar arithmetic operations"""
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    scalar_value = [float(node.get("attrs", {}).get("scalar", 1))]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    scalar_value = [float(attrs.get("scalar", 1))]
 
     input_name_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_name_id].name
@@ -1239,14 +1175,10 @@ def convert_argmax(node, **kwargs):
     """Map MXNet's argmax operator attributes to onnx's ArgMax operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    input_node_idx = kwargs["index_lookup"][node_inputs[0][0]]
+    input_node_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_idx].name
-    name = node["name"]
-    attrs = node["attrs"]
 
     axis = int(attrs.get("axis"))
     keepdims = int(attrs.get("keepdims")) if "keepdims" in attrs  else 1
@@ -1266,14 +1198,10 @@ def convert_argmin(node, **kwargs):
     """Map MXNet's argmin operator attributes to onnx's ArgMin operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    input_node_idx = kwargs["index_lookup"][node_inputs[0][0]]
+    input_node_idx = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_idx].name
-    name = node["name"]
-    attrs = node["attrs"]
 
     axis = int(attrs.get("axis"))
     keepdims = int(attrs.get("keepdims")) if "keepdims" in attrs  else 1
@@ -1293,17 +1221,13 @@ def convert_maximum(node, **kwargs):
     """Map MXNet's _maximum operator attributes to onnx's Max operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_list = []
-    for node_input in node_inputs:
+    for node_input in inputs:
         node_id = kwargs["index_lookup"][node_input[0]]
         input_node_list.append(proc_nodes[node_id].name)
 
-    name = node["name"]
-
     node = onnx.helper.make_node(
         'Max',
         inputs=input_node_list,
@@ -1319,17 +1243,13 @@ def convert_minimum(node, **kwargs):
     """Map MXNet's _minimum operator attributes to onnx's Min operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    proc_nodes = kwargs["proc_nodes"]
-    node_inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     input_node_list = []
-    for node_input in node_inputs:
+    for node_input in inputs:
         node_id = kwargs["index_lookup"][node_input[0]]
         input_node_list.append(proc_nodes[node_id].name)
 
-    name = node["name"]
-
     node = onnx.helper.make_node(
         'Min',
         inputs=input_node_list,
@@ -1345,15 +1265,12 @@ def convert_min(node, **kwargs):
     """Map MXNet's min operator attributes to onnx's ReduceMin operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    mx_axis = node.get("attrs", {}).get("axis", None)
+    mx_axis = attrs.get("axis", None)
     axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
 
-    keepdims = int(node.get("attrs", {}).get("keepdims", 0))
+    keepdims = int(attrs.get("keepdims", 0))
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1386,15 +1303,12 @@ def convert_max(node, **kwargs):
     """Map MXNet's max operator attributes to onnx's ReduceMax operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    mx_axis = node.get("attrs", {}).get("axis", None)
+    mx_axis = attrs.get("axis", None)
     axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
 
-    keepdims = int(node.get("attrs", {}).get("keepdims", 0))
+    keepdims = int(attrs.get("keepdims", 0))
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1427,15 +1341,12 @@ def convert_mean(node, **kwargs):
     """Map MXNet's mean operator attributes to onnx's ReduceMean operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    mx_axis = node.get("attrs", {}).get("axis", None)
+    mx_axis = attrs.get("axis", None)
     axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
 
-    keepdims = int(node.get("attrs", {}).get("keepdims", 0))
+    keepdims = int(attrs.get("keepdims", 0))
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1468,15 +1379,12 @@ def convert_prod(node, **kwargs):
     """Map MXNet's prod operator attributes to onnx's ReduceProd operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
-    mx_axis = node.get("attrs", {}).get("axis", None)
+    mx_axis = attrs.get("axis", None)
     axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None
 
-    keepdims = int(node.get("attrs", {}).get("keepdims", 0))
+    keepdims = int(attrs.get("keepdims", 0))
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1510,10 +1418,7 @@ def convert_elementwise_add(node, **kwargs):
     """Map MXNet's elemwise_add operator attributes to onnx's Add operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1536,10 +1441,7 @@ def covert_broadcast_add(node, **kwargs):
     """Map MXNet's broadcast_add operator attributes to onnx's Add operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1562,10 +1464,7 @@ def convert_elementwise_sub(node, **kwargs):
     """Map MXNet's elemwise_sub operator attributes to onnx's Sub operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1587,10 +1486,7 @@ def covert_broadcast_sub(node, **kwargs):
     """Map MXNet's broadcast_sub operator attributes to onnx's Sub operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1613,10 +1509,7 @@ def convert_elemwise_mul(node, **kwargs):
     """Map MXNet's elemwise_mul operator attributes to onnx's Mul operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1638,10 +1531,7 @@ def convert_broadcast_mul(node, **kwargs):
     """Map MXNet's broadcast_mul operator attributes to onnx's Mul operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1664,10 +1554,7 @@ def convert_elemwise_div(node, **kwargs):
     """Map MXNet's elemwise_div operator attributes to onnx's Div operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1690,10 +1577,7 @@ def convert_broadcast_div(node, **kwargs):
     """Map MXNet's broadcast_div operator attributes to onnx's Div operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -1716,10 +1600,7 @@ def convert_negative(node, **kwargs):
     """Map MXNet's negative operator attributes to onnx's Neg operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
 
@@ -1740,10 +1621,7 @@ def convert_abs(node, **kwargs):
     """Map MXNet's abs operator attributes to onnx's Abs operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
 
@@ -1764,10 +1642,7 @@ def convert_addn(node, **kwargs):
     """Map MXNet's add_n operator attributes to onnx's Sum operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_list = []
     for input_val in inputs:
@@ -1787,10 +1662,7 @@ def convert_ceil(node, **kwargs):
     """Map MXNet's ceil operator attributes to onnx's Ceil operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1808,10 +1680,7 @@ def convert_floor(node, **kwargs):
     """Map MXNet's floor operator attributes to onnx's Floor operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1831,11 +1700,7 @@ def convert_reshape(node, **kwargs):
     Converts output shape attribute to output shape tensor
     and return multiple created nodes.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     output_shape_list = convert_string_to_list(attrs["shape"])
 
@@ -1880,11 +1745,9 @@ def convert_cast(node, **kwargs):
     """Map MXNet's Cast operator attributes to onnx's Cast operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    dtype = node["attrs"]["dtype"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    dtype = attrs["dtype"]
 
     # dtype can be mapped only with types from TensorProto
     # float32 is mapped to float and float64 to double in onnx
@@ -1912,16 +1775,14 @@ def convert_slice_axis(node, **kwargs):
     """Map MXNet's slice_axis operator attributes to onnx's Slice operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    axes = int(node["attrs"]["axis"])
-    starts = int(node["attrs"]["begin"])
-    if node["attrs"]["end"] == 'None':
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    axes = int(attrs["axis"])
+    starts = int(attrs["begin"])
+    if attrs["end"] == 'None':
         raise ValueError("Slice: ONNX doesnt't support 'None' in 'end' attribute")
     else:
-        ends = int(node["attrs"]["end"])
+        ends = int(attrs["end"])
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1944,13 +1805,11 @@ def convert_slice_channel(node, **kwargs):
     operator based on squeeze_axis attribute
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    num_outputs = int(node.get("attrs", {})["num_outputs"])
-    axis = int(node.get("attrs", {}).get("axis", 1))
-    squeeze_axis = int(node.get("attrs", {}).get("squeeze_axis", 0))
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    num_outputs = int(attrs["num_outputs"])
+    axis = int(attrs.get("axis", 1))
+    squeeze_axis = int(attrs.get("squeeze_axis", 0))
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -1984,11 +1843,9 @@ def convert_expand_dims(node, **kwargs):
     """Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    axis = int(node["attrs"]["axis"])
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    axis = int(attrs["axis"])
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2007,12 +1864,10 @@ def convert_squeeze(node, **kwargs):
     """Map MXNet's squeeze operator attributes to onnx's squeeze operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    if "axis" in node["attrs"]:
-        axis = convert_string_to_list(node["attrs"]["axis"])
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
+
+    if "axis" in attrs:
+        axis = convert_string_to_list(attrs["axis"])
     else:
         raise AttributeError("Missing axis attribute: ONNX currently requires axis to "
                              "be specified for squeeze operator")
@@ -2035,10 +1890,7 @@ def convert_log(node, **kwargs):
     """Map MXNet's log operator attributes to onnx's Log operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2057,10 +1909,7 @@ def convert_reciprocal(node, **kwargs):
     """Map MXNet's reciprocal operator attributes to onnx's Reciprocal operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2078,10 +1927,7 @@ def convert_power(node, **kwargs):
     """Map MXNet's _power operator attributes to onnx's Pow operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -2102,10 +1948,7 @@ def convert_broadcast_power(node, **kwargs):
     """Map MXNet's _power operator attributes to onnx's Pow operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_b_id = kwargs["index_lookup"][inputs[1][0]]
@@ -2126,10 +1969,7 @@ def convert_sqrt(node, **kwargs):
     """Map MXNet's sqrt operator attributes to onnx's Sqrt operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2147,11 +1987,7 @@ def convert_depthtospace(node, **kwargs):
     """Map MXNet's depth_to_space operator attributes to onnx's
     DepthToSpace operator and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2172,11 +2008,7 @@ def convert_spacetodepth(node, **kwargs):
     """Map MXNet's space_to_depth operator attributes to onnx's
     SpaceToDepth operator and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     input_node = proc_nodes[input_node_id].name
@@ -2197,10 +2029,7 @@ def convert_square(node, **kwargs):
     """Map MXNet's square operator attributes to onnx's Pow operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
+    onnx, name, proc_nodes, inputs, _ = get_inputs(node, kwargs)
 
     input_node_a_id = kwargs["index_lookup"][inputs[0][0]]
     input_node_a = proc_nodes[input_node_a_id].name
@@ -2233,11 +2062,7 @@ def convert_sum(node, **kwargs):
     """Map MXNet's sum operator attributes to onnx's ReduceSum operator
     and return the created node.
     """
-    onnx = import_onnx_modules()
-    name = node["name"]
-    proc_nodes = kwargs["proc_nodes"]
-    inputs = node["inputs"]
-    attrs = node["attrs"]
+    onnx, name, proc_nodes, inputs, attrs = get_inputs(node, kwargs)
 
     mx_axis = attrs.get("axis", None)
     axes = convert_string_to_list(str(mx_axis)) if mx_axis is not None else None