TF_AddInput(desc, l);
  TF_AddInput(desc, r);
  return TF_FinishOperation(desc, s);
}

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s) {
  TF_OperationDescription* desc =
      TF_NewOperation(graph, "RandomUniform", "random_uniform");
  TF_AddInput(desc, {shape, 0});
  TF_SetAttrType(desc, "dtype", dtype);
  return TF_FinishOperation(desc, s);
}

        *   `tf.data.experimental.MapVectorizationOptions.*`
        *   `tf.data.experimental.OptimizationOptions.filter_with_random_uniform_fusion`
        *   `tf.data.experimental.OptimizationOptions.hoist_random_uniform`
        *   `tf.data.experimental.OptimizationOptions.map_vectorization` *
            `tf.data.experimental.OptimizationOptions.reorder_data_discarding_ops`
*   `tf.keras`:

TF_Operation* Neg(TF_Operation* n, TF_Graph* graph, TF_Status* s,
                  const char* name = "neg");

TF_Operation* LessThan(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s);

TF_Operation* RandomUniform(TF_Operation* shape, TF_DataType dtype,
                            TF_Graph* graph, TF_Status* s);

// Split `input` along the first dimension into 3 tensors

  TF_Tensor* tensor_shape = Int32Tensor({37, 1});
  TF_Operation* shape = Const(tensor_shape, func_graph.get(), s.get(), "shape");
  TF_Operation* random =
      RandomUniform(shape, TF_FLOAT, func_graph.get(), s.get());

  TF_Output outputs[] = {{random, 0}};
  *func = TF_GraphToFunction(func_graph.get(), name,
                             /*append_hash_to_fn_name=*/false, -1,