float value) {
  desc->node_builder.Attr(attr_name, value);
}

void TF_SetAttrFloatList(TF_OperationDescription* desc, const char* attr_name,
                         const float* values, int num_values) {
  desc->node_builder.Attr(attr_name,
                          ArraySlice<const float>(values, num_values));
}

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {

      op_->node_builder.Attr(attr_name, v);
    }
    return absl::OkStatus();
  }
  Status SetAttrFloatList(const char* attr_name, const float* values,
                          int num_values) override {
    op_->node_builder.Attr(attr_name,
                           ArraySlice<const float>(values, num_values));
    return absl::OkStatus();
  }
  Status SetAttrIntList(const char* attr_name, const int64_t* values,

          i_vector.push_back(default_value.list().i(i));
        }
        TFE_OpSetAttrIntList(op, attr_name, i_vector.data(), i_size);
      }
      // Float
      if (const int f_size = default_value.list().f_size()) {
        absl::InlinedVector<float, 4> f_vector;
        f_vector.reserve(f_size);
        for (int i = 0; i < f_size; ++i) {
          f_vector.push_back(default_value.list().f(i));
        }

TEST(CAPI, TestFromProto) {
  Tensor t_cc(DT_FLOAT, TensorShape({2, 3}));
  t_cc.flat<float>().setConstant(1.0);
  tensorflow::TensorProto t_proto;
  t_cc.AsProtoField(&t_proto);

  TF_Buffer* t_buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(t_proto, t_buffer));

  const int num_bytes = 6 * sizeof(float);
  float* values =
      reinterpret_cast<float*>(tensorflow::cpu_allocator()->AllocateRaw(