%0 = "tf.XlaCallModule"(%arg0) {Sout = [#tf_type.shape<*>], device = "", dim_args_spec = [], function_list = [], disabled_checks = [], has_token_input_output = false, module = "ML\EFR\03MLIRxxx-trunk\00\01\17\05\01\05\01\03\05\03\07\07\t\0B\03K5\07\01\1B\07\0B\13\0B3\0B\0B\0B\0B\0F\0B\13\0B\03\1B\0F\1B\0B\0B\0B\0B\0B\0F\13\0B\0B\0B\0B\03\07\0F\17\07\02\A7\1F\05\0D\03\03\03\07\05\0F\03\0B\0B\1B\0D'\0F)\031\113\05\11\05\13\05\15\05\17\1D\15\17\05\19\17\19\EF\01\05\1B\03\03\1D\0D\05\1F!#%\1D...

  // implementations for UniformQuantize in TF kernel and the lowering passes
  // may cause +/-1 differences.
  ExecuteAndCompareResultsWithTfKernel(
      kProgram, {&arg0}, /*tf_program=*/std::nullopt, /*error_tolerance=*/0.35);
}

TEST_F(ConvertTfQuantToMhloIntTest, UniformQuantizePerChannelToValidGraph) {
  constexpr absl::string_view kProgram = R"mlir(
func.func @main(

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