return;
    }
  } else if (S_ISDIR(st.st_mode)) {
    TF_SetStatus(status, TF_FAILED_PRECONDITION, "target path is a directory");
    return;
  }

  // We cannot rename directories yet, so prevent this.
  if (stat(src, &st) != 0) {
    TF_SetStatusFromIOError(status, errno, src);
    return;
  } else if (S_ISDIR(st.st_mode)) {

  EXPECT_TRUE(neg == neg2);
  NodeDef node_def2;
  ASSERT_TRUE(GetNodeDef(neg2, &node_def2));
  EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());

  TF_Operation* feed2 = TF_GraphOperationByName(graph, "feed");
  EXPECT_TRUE(feed == feed2);
  ASSERT_TRUE(GetNodeDef(feed, &node_def));
  ASSERT_TRUE(GetNodeDef(feed2, &node_def2));
  EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());

            create status using an error code without accessing it.
        *   Use the free functions such as
            `tensorflow::errors::IsInvalidArgument` if needed.
        *   In the last resort, use
            e.g.`static_cast<tensorflow::errors::Code>(error::Code::INVALID_ARGUMENT)`
            or `static_cast<int>(code)` for comparisons.

    visibility = ["//visibility:public"],
)

config_setting(
    name = "debug",
    values = {
        "compilation_mode": "dbg",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "optimized",
    values = {
        "compilation_mode": "opt",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "arm",

using tensorflow::FunctionDef;
using tensorflow::Node;
using tensorflow::NodeBuilder;
using tensorflow::Status;
using tensorflow::errors::InvalidArgument;

namespace {
typedef std::unique_ptr<TF_Function, decltype(&TF_DeleteFunction)>
    UniqueFuncPtr;
}

// struct TF_Operation { tensorflow::Node node; };
static TF_Operation* ToTF_Operation(Node* node) {
  return static_cast<TF_Operation*>(static_cast<void*>(node));

#     cuda_clang:             Use Clang when building CUDA code.
#     avx_linux:              Build with avx instruction set on linux.
#     avx_win:                Build with avx instruction set on windows
#
# Other build options:
#     short_logs:       Only log errors during build, skip warnings.
#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.

// without using tensorflow python code, hence left unspecified here.
//
// Gradient is the type returned by gradient functions. In Python TF it's either
// Tensor or IndexedSlices or None, which here we map to nullptr. Gradients need
// to allow their size to be computed and they need to be passable to a backward
// function and deleted (as the backprop code creates lots of gradients the user
// is not interested in).
//

#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/node_def_util.h"
#include "tensorflow/core/framework/rendezvous.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/casts.h"

      // added since the last TF_SessionRun() call.
      for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) {
        Node* const node = graph.FindNodeId(id);
        if (node != nullptr && node->IsOp()) {
          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();

interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.

Note that the file `__init__.py` in the TensorFlow source code tree is actually
only a placeholder to enable test cases to run. The TensorFlow build replaces