type* typed_a = static_cast<type*>(a);               \
    type* typed_b = static_cast<type*>(b);               \
    for (int64_t i = 0; i < num_elements; ++i) {         \
      if (DataTypeIsFloating(dtype)) {                   \
        EXPECT_FLOAT_EQ(static_cast<float>(typed_a[i]),  \
                        static_cast<float>(typed_b[i])); \
      } else {                                           \

                                .FirstN(1000)
                                .Summarize(-1));
        new_output = print_op;
      }

      if (debugging_opts.check_output_numerics &&
          DataTypeIsFloating(new_output.type())) {
        ops::CheckNumerics check_numerics_op(
            s.WithOpName("check_output_", oidx)
                .WithDevice(new_node->requested_device())

namespace {

static Status SafeConj(AbstractContext* ctx, AbstractTensorHandle* const input,
                       AbstractTensorHandle** output, const char* name) {
  auto dtype = input->DataType();
  if (DataTypeIsFloating(BaseType(dtype)) ||
      DataTypeIsInteger(BaseType(dtype))) {
    return tensorflow::ops::Identity(ctx, input, output, name);
  } else if (!DataTypeIsComplex(BaseType(dtype)) &&