diff --git a/tensorflow/lite/kernels/transpose_conv.cc b/tensorflow/lite/kernels/transpose_conv.cc
index 7a2b1a8dceb..497edac5762 100644
--- a/tensorflow/lite/kernels/transpose_conv.cc
+++ b/tensorflow/lite/kernels/transpose_conv.cc
@@ -80,8 +80,6 @@ struct OpData {
   int output_shift;
 
   // Per channel output multiplier and shift.
-  // TODO(b/144846950): Add channel dimension index for the kernel to be more
-  // flexible.
   std::vector<int32_t> per_channel_output_multiplier;
   std::vector<int32_t> per_channel_output_shift;
 
@@ -374,17 +372,20 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
     const auto* affine_quantization =
         reinterpret_cast<TfLiteAffineQuantization*>(
             weights->quantization.params);
+    const int channels_out = weights->dims->data[0];
     TF_LITE_ENSURE(context, affine_quantization);
     TF_LITE_ENSURE(context, affine_quantization->scale);
-    const int number_channel = affine_quantization->scale->size;
-    data->per_channel_output_multiplier.resize(number_channel);
-    data->per_channel_output_shift.resize(number_channel);
+    TF_LITE_ENSURE(context, (affine_quantization->scale->size == 1 ||
+                             affine_quantization->scale->size == channels_out));
+
+    data->per_channel_output_multiplier.resize(channels_out);
+    data->per_channel_output_shift.resize(channels_out);
     TF_LITE_ENSURE_STATUS(tflite::PopulateConvolutionQuantizationParams(
         context, input, weights, bias, output, kTfLiteActNone,
         &data->output_multiplier, &data->output_shift,
         &data->output_activation_min, &data->output_activation_max,
         data->per_channel_output_multiplier.data(),
-        data->per_channel_output_shift.data()));
+        data->per_channel_output_shift.data(), channels_out));
   }
 
   return kTfLiteOk;
diff --git a/tensorflow/lite/kernels/transpose_conv_test.cc b/tensorflow/lite/kernels/transpose_conv_test.cc
index c4d79036ca5..08b516fa8cf 100644
--- a/tensorflow/lite/kernels/transpose_conv_test.cc
+++ b/tensorflow/lite/kernels/transpose_conv_test.cc
@@ -462,6 +462,37 @@ TEST_P(TransposeConvOpTest, TestQuantizedPerChannelMultiChannel) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 5, 5, 2}));
 }
 
+// Test data copied from the float multi-channel test above.
+TEST_P(TransposeConvOpTest, TestQuantizedPerTensorMultiChannel) {
+  const std::initializer_list<float> filter_data = {
+      1, 3, 5, 7, 9, 11, 13, 15, 17, 2, 4, 6, 8, 10, 12, 14, 16, 18};
+  const std::initializer_list<int8_t> const_filter_data = {
+      7,  21, 35, 49, 64, 78, 92, 106, 120,
+      14, 28, 42, 56, 71, 85, 99, 113, 127};
+  PerChannelQuantizedTransposeConvOpModel model(
+      GetRegistration(), {1, 5, 5, 2},
+      {TensorType_INT8, {2, 3, 3, 1}, 0, 0, 0, 0, true, {18.0 / 127}, {0}, 0},
+      const_filter_data, {TensorType_INT8, {1, 2, 2, 1}, 0, 0, 4.0 / 255, -128},
+      {TensorType_INT8, {}, 0, 0, 1, -128}, Padding_VALID, 2, 2, GetTestType(),
+      /* version */ 2);
+  model.SetInput({1, 2, 3, 4});
+  if (GetTestType() == TestType::kDynamic) {
+    model.SetFilter(filter_data);
+  }
+  model.Invoke();
+
+  EXPECT_THAT(
+      model.GetDequantizedOutput(),
+      ElementsAreArray(ArrayFloatNear(
+          {1,  2,  3,  4,  7,  10, 6,  8,  10, 12, 7,   8,   9,  10, 25, 28, 18,
+           20, 22, 24, 16, 20, 24, 28, 62, 72, 42, 48,  54,  60, 21, 24, 27, 30,
+           61, 68, 36, 40, 44, 48, 39, 42, 45, 48, 103, 110, 60, 64, 68, 72},
+          1e-5)));
+
+  // GetOutputShape() should always be same as model.SetOutputShape(...);
+  EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({1, 5, 5, 2}));
+}
+
 class PerChannelQuantizedTransposeConvOpModel16x8
     : public BaseTransposeConvOpModel<int16_t> {
  public: