FrozenGene commented on a change in pull request #4695: 
[Relay][Frontend][TFlite] Add parser support for relational ops
URL: https://github.com/apache/incubator-tvm/pull/4695#discussion_r371276409
 
 

 ##########
 File path: python/tvm/relay/frontend/tflite.py
 ##########
 @@ -705,47 +710,77 @@ def convert_div(self, op):
         # Check if the input tensor is quantized, call QNN op
         if self.is_quantized(op):
             raise tvm.error.OpNotImplemented(
-                'TFlite quantized div operator is not supported yet.')
+                'TFlite quantized DIV operator is not supported yet.')
         return self._convert_elemwise(_op.divide, op)
 
     def convert_pow(self, op):
         # Check if the input tensor is quantized, call QNN op
         if self.is_quantized(op):
             raise tvm.error.OpNotImplemented(
-                'TFlite quantized pow operator is not supported yet.')
+                'TFlite quantized POW operator is not supported yet.')
         return self._convert_elemwise(_op.power, op)
 
+    def convert_squared_difference(self, op):
+        # Check if the input tensor is quantized, call QNN op
+        if self.is_quantized(op):
+            raise tvm.error.OpNotImplemented(
+                'TFlite quantized SQUARED_DIFFERENCE operator is not supported 
yet.')
+        difference = self._convert_elemwise(_op.subtract, op)
+        # _convert_elemwise has guaranteed only have one output tensor
+        exp_type = 
self.get_tensor_type_str(self.get_output_tensors(op)[0].tensor.Type())
+        out = _op.power(difference, relay.const(2, exp_type))
+        return out
+
     def convert_maximum(self, op):
         # Check if the input tensor is quantized, call QNN op
         if self.is_quantized(op):
             raise tvm.error.OpNotImplemented(
-                'TFlite quantized maximum operator is not supported yet.')
+                'TFlite quantized MAXIMUM operator is not supported yet.')
         return self._convert_elemwise(_op.maximum, op)
 
     def convert_minimum(self, op):
         # Check if the input tensor is quantized, call QNN op
         if self.is_quantized(op):
             raise tvm.error.OpNotImplemented(
-                'TFlite quantized minimum operator is not supported yet.')
+                'TFlite quantized MINIMUM operator is not supported yet.')
         return self._convert_elemwise(_op.minimum, op)
 
     def convert_greater(self, op):
         # Check if the input tensor is quantized, call QNN op
         if self.is_quantized(op):
             raise tvm.error.OpNotImplemented(
-                'TFlite quantized greater operator is not supported yet.')
+                'TFlite quantized GREATER operator is not supported yet.')
         return self._convert_elemwise(_op.greater, op)
 
-    def convert_squared_difference(self, op):
-        # Check if the input tensor is quantized, call QNN op
+    def convert_greater_equal(self, op):
 
 Review comment:
   add such a doc string to every elemwise function.

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