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diff --git a/tests/nnapi/specs/skip/V1_2/mul_v1_2.mod.py b/tests/nnapi/specs/skip/V1_2/mul_v1_2.mod.py
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+#
+# Copyright (C) 2018 The Android Open Source Project
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# TEST 1: MUL float16
+model = Model()
+i1 = Input("op1", "TENSOR_FLOAT16", "{3}") # a vector of 3 float16s
+i2 = Input("op2", "TENSOR_FLOAT16", "{3}") # another vector of 3 float16s
+act = Int32Scalar("act", 0) # an int32_t scalar activation
+i3 = Output("op3", "TENSOR_FLOAT16", "{3}")
+model = model.Operation("MUL", i1, i2, act).To(i3)
+model = model.RelaxedExecution(False)
+
+# Example 1. Input in operand 0,
+input0 = {i1: # input 0
+          [1.0009765625, 1.0, 2.5],
+          i2: # input 1
+          [2, 0.0001, 3.5]}
+
+output0 = {i3: # output 0
+           [2.001953125, 0.0001000165, 8.75]}
+
+# Instantiate an example
+Example((input0, output0))
+
+
+# TEST 2: MUL broadcast float16
+model = Model()
+i1 = Input("op1", "TENSOR_FLOAT16", "{1, 2}")
+i2 = Input("op2", "TENSOR_FLOAT16", "{2, 2}")
+act = Int32Scalar("act", 0)
+i3 = Output("op3", "TENSOR_FLOAT16", "{2, 2}")
+model = model.Operation("MUL", i1, i2, act).To(i3)
+
+# Example 1. Input in operand 0,
+input0 = {i1: # input 0
+          [1, 2],
+          i2: # input 1
+          [1, 2, 3, 4]}
+
+output0 = {i3: # output 0
+           [1, 4, 3, 8]}
+
+# Instantiate an example
+Example((input0, output0))
+
+
+# TEST 3: MUL, zero-sized input
+
+# Use BOX_WITH_NMS_LIMIT op to generate a zero-sized internal tensor for box cooridnates.
+p1 = Parameter("scores", "TENSOR_FLOAT32", "{1, 2}", [0.90, 0.10]) # scores
+p2 = Parameter("roi", "TENSOR_FLOAT32", "{1, 8}", [1, 1, 10, 10, 0, 0, 10, 10]) # roi
+o1 = Output("scoresOut", "TENSOR_FLOAT32", "{0}") # scores out
+o2 = Output("classesOut", "TENSOR_INT32", "{0}") # classes out
+tmp1 = Internal("roiOut", "TENSOR_FLOAT32", "{0, 4}") # roi out
+tmp2 = Internal("batchSplitOut", "TENSOR_INT32", "{0}") # batch split out
+model = Model("zero_sized").Operation("BOX_WITH_NMS_LIMIT", p1, p2, [0], 0.3,  -1, 0, 0.4, 1.0, 0.3).To(o1, tmp1, o2, tmp2)
+
+# Use ROI_ALIGN op to convert into zero-sized feature map.
+layout = BoolScalar("layout", False) # NHWC
+i1 = Input("in", "TENSOR_FLOAT32", "{1, 1, 1, 2}")
+zero_sized = Internal("featureMap", "TENSOR_FLOAT32", "{0, 2, 2, 2}")
+model = model.Operation("ROI_ALIGN", i1, tmp1, tmp2, 2, 2, 2.0, 2.0, 4, 4, layout).To(zero_sized)
+
+# MUL op with numBatches = 0.
+i2 = Parameter("op", "TENSOR_FLOAT32", "{1, 2, 2, 1}", [1, 2, 3, 4]) # weights
+o3 = Output("out", "TENSOR_FLOAT32", "{0, 2, 2, 2}") # out
+model = model.Operation("MUL", zero_sized, i2, 0).To(o3)
+
+quant8 = DataTypeConverter().Identify({
+    p1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    p2: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+    o1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    tmp1: ("TENSOR_QUANT16_ASYMM", 0.125, 0),
+    i1: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    zero_sized: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    i2: ("TENSOR_QUANT8_ASYMM", 0.1, 128),
+    o3: ("TENSOR_QUANT8_ASYMM", 0.1, 128)
+})
+
+# Create test case with dummy values.
+Example({
+    i1: [1, 2],
+    o1: [0],
+    o2: [0],
+    o3: [0],
+}).AddVariations("relaxed", quant8, "float16")