codeislife99 commented on a change in pull request #7442:
URL: https://github.com/apache/tvm/pull/7442#discussion_r576396099



##########
File path: src/relay/op/tensor/transform.cc
##########
@@ -1584,6 +1584,47 @@ RELAY_REGISTER_OP("repeat")
     .set_attr<FTVMCompute>("FTVMCompute", RepeatCompute)
     .set_attr<TOpPattern>("TOpPattern", kBroadcast);
 
+bool SparseFillEmptyRowsRel(const Array<Type>& types, int num_inputs, const 
Attrs& attrs,
+                            const TypeReporter& reporter) {
+  // types: [sparse_indices, sparse_values, dense_shape, default_value, result]
+  ICHECK_EQ(types.size(), 5);
+  std::vector<Type> fields;
+  auto sparse_indices = types[0].as<TensorTypeNode>();
+  auto ndims = sparse_indices->shape[1];
+  fields.push_back(TensorType(Array<PrimExpr>{Any(), ndims}, 
tvm::DataType::Int(64)));
+  fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+  fields.push_back(TensorType(Array<PrimExpr>{Any()}, tvm::DataType::Int(64)));
+  reporter->Assign(types[types.size() - 1], TupleType(Array<Type>(fields)));
+  return true;
+}
+
+Expr MakeSparseFillEmptyRows(Expr sparse_indices, Expr sparse_values, Expr 
dense_shape,
+                             Expr default_value) {
+  static const Op& op = Op::Get("sparse_fill_empty_rows");
+  return Call(op, {sparse_indices, sparse_values, dense_shape, default_value}, 
Attrs(), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op._make.sparse_fill_empty_rows")
+    .set_body_typed(MakeSparseFillEmptyRows);
+
+RELAY_REGISTER_OP("sparse_fill_empty_rows")
+    .describe(R"code(Return representation of a sparse tensor with empty rows 
filled with default 
+    value.)code" TVM_ADD_FILELINE)
+    .set_num_inputs(4)
+    .add_argument("sparse_indices", "Tensor",
+                  "A 2-D int64 tensor of shape [N, ndims], which specifies the 
indices of the"
+                  "elements in the sparse tensor that contain nonzero values")
+    .add_argument("sparse_values", "Tensor",
+                  "A 1-D tensor[N] which supplies the values for each element 
in indices")
+    .add_argument("dense_shape", "Tensor",

Review comment:
       I just copied the terminology from TF documentation. What do you think 
would be a good name ? 




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