],
)

tf_cc_test(
    name = "framework_gradients_test",
    srcs = ["framework/gradients_test.cc"],
    deps = [
        ":cc_ops",
        ":client_session",
        ":grad_op_registry",
        ":grad_ops",
        ":gradients",
        ":testutil",
        "//tensorflow/core:all_kernels",
        "//tensorflow/core:framework",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core:protos_all_cc",

  // The "truth" labels are at index 1.
  auto softmax_grad = op.output(1);

  // The loss is the output at index 0, and backprop is the output at index 1.
  auto grad_loss = grad_inputs[0];
  auto grad_grad = grad_inputs[1];

  auto grad = BroadcastMul(scope, grad_loss, softmax_grad);
  if (!IsZero(scope, grad_grad)) {
    std::vector<int> axis;
    auto logits_softmax = Softmax(scope, logits);

  int axis;
  TF_RETURN_IF_ERROR(GetNodeAttr(op.node()->attrs(), "axis", &axis));

  grad_outputs->reserve(N);
  auto grad_op = Unstack(scope, grad_inputs[0], N, Unstack::Axis(axis));
  for (const Output& o : grad_op.output) {
    grad_outputs->emplace_back(o);
  }
  return scope.status();
}
REGISTER_GRADIENT_OP("Pack", PackGrad);

    // safe.
    if (auto tensor_array_v3_op = dyn_cast<TF::TensorArrayV3Op>(tf_op)) {
      // The "flow" in TensorArrayV3 is always a scalar float tensor.
      // https://www.tensorflow.org/api_docs/python/tf/raw_ops/TensorArrayWriteV3
      Value flow = tensor_array_v3_op.getFlow();
      Type scalar_f32_tensor_type =
          RankedTensorType::get(/*shape=*/{}, rewriter.getF32Type());
      flow.setType(scalar_f32_tensor_type);

  // possible terminator.
  return std::next(block->begin()) == block->end() ||
         std::next(block->begin(), 2) == block->end();
}

// This pattern matches GraphOps with only one FetchOp (empty) and remaps the
// results of the GraphOp to the operands of the FetchOp.
struct DropEmptyGraph : public OpRewritePattern<GraphOp> {
  using OpRewritePattern<GraphOp>::OpRewritePattern;