diff --git a/tensorflow/compiler/mlir/lite/flatbuffer_export.cc b/tensorflow/compiler/mlir/lite/flatbuffer_export.cc
index 7ca83a636a7..e5931cd73f6 100644
--- a/tensorflow/compiler/mlir/lite/flatbuffer_export.cc
+++ b/tensorflow/compiler/mlir/lite/flatbuffer_export.cc
@@ -435,7 +435,7 @@ class Translator {
   // Builds operator for the given operation with specified operand and result
   // tensor indices. Emits an error and returns llvm::None on failure.
   Optional<BufferOffset<tflite::Operator>> BuildOperator(
-      Operation* inst, const std::vector<int32_t>& operands,
+      Operation* inst, std::vector<int32_t> operands,
       const std::vector<int32_t>& results,
       const std::vector<int32_t>& intermediates);
 
@@ -927,7 +927,7 @@ uint32_t Translator::GetOpcodeIndex(const std::string& op_name,
 }
 
 Optional<BufferOffset<tflite::Operator>> Translator::BuildOperator(
-    Operation* inst, const std::vector<int32_t>& operands,
+    Operation* inst, std::vector<int32_t> operands,
     const std::vector<int32_t>& results,
     const std::vector<int32_t>& intermediates) {
   const auto* dialect = inst->getDialect();
@@ -981,6 +981,15 @@ Optional<BufferOffset<tflite::Operator>> Translator::BuildOperator(
 
     std::string op_name = inst->getName().getStringRef().str();
     uint32_t opcode_index = GetOpcodeIndex(op_name, *builtin_code);
+
+    // If this is TransposeConv we need to do a special case of ignoring the
+    // optional tensor, to allow newly created models to run on old runtimes.
+    if (*builtin_code == tflite::BuiltinOperator_TRANSPOSE_CONV) {
+      if (operands.size() == 4 && operands.at(3) == -1) {
+        operands.pop_back();
+      }
+    }
+
     auto offset = CreateFlatBufferOperator(inst, opcode_index, operands,
                                            results, intermediates, &builder_);
     if (!offset) {
diff --git a/tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/transpose_conv_optional.mlir b/tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/transpose_conv_optional.mlir
new file mode 100644
index 00000000000..621d10d9000
--- /dev/null
+++ b/tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/transpose_conv_optional.mlir
@@ -0,0 +1,77 @@
+// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck --dump-input-on-failure %s
+
+func @main(%arg0: tensor<4xi32>, %arg1: tensor<32x4x4x128xf32>, %arg2: tensor<1x32x42x128xf32>) -> tensor<1x64x84x32xf32> {
+// CHECK: {
+// CHECK-NEXT:  version: 3,
+// CHECK-NEXT:  operator_codes: [ {
+// CHECK-NEXT:    builtin_code: TRANSPOSE_CONV,
+// CHECK-NEXT:    version: 1
+// CHECK-NEXT:  } ],
+// CHECK-NEXT:  subgraphs: [ {
+// CHECK-NEXT:    tensors: [ {
+// CHECK-NEXT:      shape: [ 4 ],
+// CHECK-NEXT:      type: INT32,
+// CHECK-NEXT:      buffer: 1,
+// CHECK-NEXT:      name: "arg0",
+// CHECK-NEXT:      quantization: {
+// CHECK-EMPTY:
+// CHECK-NEXT:      }
+// CHECK-NEXT:    }, {
+// CHECK-NEXT:      shape: [ 32, 4, 4, 128 ],
+// CHECK-NEXT:      buffer: 2,
+// CHECK-NEXT:      name: "arg1",
+// CHECK-NEXT:      quantization: {
+// CHECK-EMPTY:
+// CHECK-NEXT:      }
+// CHECK-NEXT:    }, {
+// CHECK-NEXT:      shape: [ 1, 32, 42, 128 ],
+// CHECK-NEXT:      buffer: 3,
+// CHECK-NEXT:      name: "arg2",
+// CHECK-NEXT:      quantization: {
+// CHECK-EMPTY:
+// CHECK-NEXT:      }
+// CHECK-NEXT:    }, {
+// CHECK-NEXT:      shape: [ 1, 64, 84, 32 ],
+// CHECK-NEXT:      buffer: 4,
+// CHECK-NEXT:      name: "tfl.transpose_conv",
+// CHECK-NEXT:      quantization: {
+// CHECK-EMPTY:
+// CHECK-NEXT:      }
+// CHECK-NEXT:    } ],
+// CHECK-NEXT:    inputs: [ 0, 1, 2 ],
+// CHECK-NEXT:    outputs: [ 3 ],
+// CHECK-NEXT:    operators: [ {
+// CHECK-NEXT:      inputs: [ 0, 1, 2 ],
+// CHECK-NEXT:      outputs: [ 3 ],
+// CHECK-NEXT:      builtin_options_type: TransposeConvOptions,
+// CHECK-NEXT:      builtin_options: {
+// CHECK-NEXT:        stride_w: 2,
+// CHECK-NEXT:        stride_h: 2
+// CHECK-NEXT:      }
+// CHECK-NEXT:    } ],
+// CHECK-NEXT:    name: "main"
+// CHECK-NEXT:  } ],
+// CHECK-NEXT:  description: "MLIR Converted.",
+// CHECK-NEXT:  buffers: [ {
+// CHECK-EMPTY:
+// CHECK-NEXT:  }, {
+// CHECK-EMPTY:
+// CHECK-NEXT:  }, {
+// CHECK-EMPTY:
+// CHECK-NEXT:  }, {
+// CHECK-EMPTY:
+// CHECK-NEXT:  }, {
+// CHECK-EMPTY:
+// CHECK-NEXT:  }, {
+// CHECK-NEXT:    data: [ 49, 46, 57, 46, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
+// CHECK-NEXT:  } ],
+// CHECK-NEXT:  metadata: [ {
+// CHECK-NEXT:    name: "min_runtime_version",
+// CHECK-NEXT:    buffer: 5
+// CHECK-NEXT:  } ]
+// CHECK-NEXT:}
+
+  %cst = constant unit
+  %0 = "tfl.transpose_conv"(%arg0, %arg1, %arg2, %cst) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<4xi32>, tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, none) -> tensor<1x64x84x32xf32>
+  return %0 : tensor<1x64x84x32xf32>
+}
diff --git a/tensorflow/lite/tools/optimize/quantize_model.cc b/tensorflow/lite/tools/optimize/quantize_model.cc
index 6c6ba10d60c..93372c6d460 100644
--- a/tensorflow/lite/tools/optimize/quantize_model.cc
+++ b/tensorflow/lite/tools/optimize/quantize_model.cc
@@ -865,18 +865,10 @@ TfLiteStatus QuantizeBiases(ModelT* model,
         continue;
       }
       for (const int bias_idx : property.biases) {
-        if (op->inputs[bias_idx] == kTfLiteOptionalTensor) {
+        if (bias_idx >= op->inputs.size() ||
+            op->inputs[bias_idx] == kTfLiteOptionalTensor) {
           continue;
         }
-        if (bias_idx >= op->inputs.size()) {
-          TF_LITE_REPORT_ERROR(
-              error_reporter,
-              "Required input index %d is larger than the input length of "
-              "op  %s at index %d in subgraph %d",
-              bias_idx, op->inputs.size(), EnumNameBuiltinOperator(op_code),
-              op_idx, subgraph_idx);
-          return kTfLiteError;
-        }
         // Quantize if it is not quantized already as the
         // output of another op or input of another op.
         TensorT* bias_tensor = subgraph->tensors[op->inputs[bias_idx]].get();
@@ -1046,7 +1038,8 @@ TfLiteStatus EnsureBiasScaleCompatibility(
 
       // Loop over all bias tensors.
       for (const int bias_idx : property.biases) {
-        if (op->inputs[bias_idx] == kTfLiteOptionalTensor) {
+        if (bias_idx >= op->inputs.size() ||
+            op->inputs[bias_idx] == kTfLiteOptionalTensor) {
           continue;
         }
         TensorT* bias_tensor = subgraph->tensors[op->inputs[bias_idx]].get();