return CartesianSet.create(sets);
  }

  /**
   * Returns every possible list that can be formed by choosing one element from each of the given
   * sets in order; the "n-ary <a href="http://en.wikipedia.org/wiki/Cartesian_product">Cartesian
   * product</a>" of the sets. For example:
   *
   * <pre>{@code
   * Sets.cartesianProduct(

    return CartesianSet.create(sets);
  }

  /**
   * Returns every possible list that can be formed by choosing one element from each of the given
   * sets in order; the "n-ary <a href="http://en.wikipedia.org/wiki/Cartesian_product">Cartesian
   * product</a>" of the sets. For example:
   *
   * <pre>{@code
   * Sets.cartesianProduct(

        https://github.com/kubernetes/kubernetes/raw/master/cluster/gce/windows/k8s-node-setup.psm1 `
        -OutFile C:\k8s-node-setup.psm1
    Invoke-WebRequest `
        https://github.com/kubernetes/kubernetes/raw/master/cluster/gce/windows/configure.ps1 `
        -OutFile C:\configure.ps1
    Import-Module -Force C:\k8s-node-setup.psm1  # -Force to override existing
    # Execute functions manually or run configure.ps1.
#>

		prologue.To = obj.Addr{Type: obj.TYPE_MEM, Reg: REG_SP, Offset: -stacksize}

		// Insert stack adjustment.
		prologue = obj.Appendp(prologue, newprog)
		prologue.As = AADDI
		prologue.From = obj.Addr{Type: obj.TYPE_CONST, Offset: -stacksize}
		prologue.Reg = REG_SP
		prologue.To = obj.Addr{Type: obj.TYPE_REG, Reg: REG_SP}
		prologue.Spadj = int32(stacksize)

          if (TF::TensorListSetItemOp set_op =
                  llvm::dyn_cast<TF::TensorListSetItemOp>(use.getOwner())) {
            element_shape = rewriter.create<TF::ShapeOp>(
                op.getLoc(),
                tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
                set_op.getItem());
            element_shape_acquired = true;

}

int FindReturnIndex(Value val) {
  const int not_found = -1;
  for (auto user : val.getUsers()) {
    if (auto ret_op = llvm::dyn_cast<func::ReturnOp>(user)) {
      for (auto index = 0; index < ret_op->getNumOperands(); ++index) {
        if (val == ret_op->getOperand(index)) {
          return index;
        }
      }
    }
    if (auto ident_op = llvm::dyn_cast<TF::IdentityOp>(user)) {

    bool reuse_existing_functions, FunctionLibraryDefinition* library) {
  // name_in is copied here because name may be modified below if
  // rewrite_subgraph_fn is true.
  string name = name_in;
  call_node_def_.set_op(name);
  call_node_def_.set_name(name);
  call_node_def_.set_device(device_);

  if (rewrite_subgraph_fn) {
    std::vector<OutputTensor> arg_source_tensors(args_by_src_.size());

  *flib_def.add_function() = std::move(func);
  TF_ASSERT_OK(root.graph()->AddFunctionLibrary(flib_def));
  NodeDef call_node;
  call_node.set_name("fn_call");
  call_node.set_op("Stateful_func");
  Status status;
  Node* call = root.graph()->AddNode(call_node, &status);
  TF_ASSERT_OK(status);

  Output shape = Output(call, 0);
  Output reshape_input =

    EXPECT_FALSE(TF_TensorIsAligned(a));
  }
  TF_DeleteTensor(a);
}

TEST(CAPI, MessageBufferConversion) {
  NodeDef node_in, node_out;
  node_in.set_name("Test name");
  node_in.set_op("Test op");

  TF_Buffer* buffer = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(node_in, buffer));
  TF_CHECK_OK(BufferToMessage(buffer, &node_out));
  TF_DeleteBuffer(buffer);