message: Improve comments and add a static cast
diff: @@ -1486,8 +1486,8 @@ class OpConverterTest : public ::testing::Test {
  // Adds ITensor for both validation and conversion. The difference compared to
  // AddTestTensorWithExplicitBatchDim is in the meaning of the dims parameter.
  // To define a tensor with NCHW shape, here we set dims = {C,H,W} and
  - // batch_size = N. TODO(tfeher) remove this once all test specify batch dim
  - // explicitly.
  + // batch_size = N. TODO(tfeher) remove this function once all test are updated
  + // to use the other version of AddTestTensor which has the trt_mode arg.
  void AddTestTensor(
  const string& name, const std::vector<int32>& dims, int batch_size = 1,
  nvinfer1::DataType trt_dtype = nvinfer1::DataType::kFLOAT) {
  @@ -1678,9 +1678,9 @@ class ParameterizedOpConverterTest
  // them would be sufficient:
  // - All valid options to TrtTestMode (implicit, explicit, dynamic shape)
  // - DataType: is the TF data type of the input tensors. This usually only
  -// influences the data type added by Converter::AddInputTensor. That function
  -// is tested separately for valid combinations of input data types, therefore
  -// for most of the converter its is sufficient to test for DT_FLOAT.
  +// influences the data type added by Converter::AddInputTensor. We test the
  +// valid combinations of input data types in AddAndGetInputs, therefore
  +// for most of the OpConverterTest its is sufficient to test for DT_FLOAT.
  // - TrtPrecisionMode: valid options are FP32, FP16 and INT8. This influences
  // how TRT handles the precision inside the TRT network, but should not matter
  // for the TF -> TRT conversion. Therefore it should be sufficient to test
  @@ -1940,7 +1940,8 @@ TEST_P(ParameterizedOpConverterTest, ConvertTranspose) {
  if (p.param.empty()) {
  AddTestTensor("weights", {3});
  } else {
  - AddTestWeights<int32>("weights", {p.param.size()}, p.param);
  + AddTestWeights<int32>("weights", {static_cast<int>(p.param.size())},
  + p.param);
  }
  RunValidationAndConversion(node_def, p.status, "my_transpose",
  p.expected_output_dims);
  @@ -3201,7 +3202,6 @@ TEST_P(ParameterizedOpConverterTest, ConvertSqueeze) {
  SCOPED_TRACE(p);
  Reset(converter_precision, trt_mode);
  NodeDef node_def = get_squeeze_nodedef(p.param);
  - bool skip_test;
  AddTestTensor("input", p.input_dims, TfDataTypeToTrt(tf_dtype), trt_mode,
  &p.partial_input_dims);
  RunValidationAndConversion(node_def, p.status, "my_squeeze",
  