// hence dx_k = dy for all x_k
  // So the gradient for AddN just transfers the incoming gradient to
  // all outgoing gradients.
  auto incoming = Identity(scope, grad_inputs[0]);
  for (int32_t i = 0; i < op.num_inputs(); ++i) {
    grad_outputs->push_back(incoming);
  }
  return scope.status();
}
REGISTER_GRADIENT_OP("AddN", AddNGrad);

Status PowGrad(const Scope& scope, const Operation& op,

      .Attr("SomeDataTypeAttr: type");

  static int num_inputs = 0;
  static int num_outputs = 0;

  // A kernel whose Compute function has a side-effect of updating num_inputs
  // and num_outputs. Various functions on TF_OpKernelContext are also
  // exercised.
  auto my_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    num_inputs = TF_NumInputs(ctx);
    num_outputs = TF_NumOutputs(ctx);

    FunctionType func_type = func.getFunctionType();
    int num_inputs = func_type.getNumInputs();
    int num_results = func_type.getNumResults();

    // For each argument type in function's arguments, change it to uranked
    // tensor type if it's a variant type.
    SmallVector<Type, 8> updated_argument_types;
    updated_argument_types.reserve(num_inputs);
    UpdateTensorListTypes<mlir::OperandRange>(

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_OpAddAttrs(ret, TFE_OpGetAttrs(other));
  int num_inputs = TFE_OpGetFlatInputCount(other, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  for (int input_index = 0; input_index < num_inputs; ++input_index) {
    TFE_TensorHandle* input = TFE_OpGetFlatInput(other, input_index, status);

// For inputs that take a list of tensors.
// inputs must point to TF_Output[num_inputs].
TF_CAPI_EXPORT extern void TF_AddInputList(TF_OperationDescription* desc,
                                           const TF_Output* inputs,
                                           int num_inputs);

// Call once per control input to `desc`.

def OptimizeToSoftmax : Pat<
  (TFL_DivOp (TFL_ExpOp:$exp $input),
             (TFL_SumOp:$sum $sum_input, (Arith_ConstantOp I32ElementsAttr: $axes),
                             ConstBoolAttrTrue), TFL_AF_None),
  (TFL_SoftmaxOp $input, ConstF32Attr<"1.0">),
  [(IsSame $exp, $sum_input),
   (AxesIsLastDimension $axes, $sum_input),
   (HasTwoUse $exp),
   (HasOneUse $sum)]>;