TracingOperationPtr operation(CreateOperation());
    TF_RETURN_IF_ERROR(operation->Reset("Placeholder", nullptr));
    TF_RETURN_IF_ERROR(
        operation->SetOpName(absl::StrCat("_input_", inputs_.size()).c_str()));
    TF_RETURN_IF_ERROR(operation->SetAttrType("dtype", dtype));
    if (!shape.unknown_rank()) {
      TF_RETURN_IF_ERROR(operation->SetAttrShape(

    TF_SetStatus(&s, static_cast<TF_Code>(result.error_code()),
                 std::string(result.error_message()).data());
    if (TF_GetCode(&s) != TF_Code::TF_OK) {
      tensorflow::CoordinationServiceError error;
      *error.mutable_source_task() = result.error_payload().source_task();
      TF_SetPayload(&s, tensorflow::CoordinationErrorPayloadKey().data(),
                    error.SerializeAsString().c_str());
    }

    // backward function under a gradient tape.
    forward_grads.resize(output_tensors.size());
    TF_RETURN_IF_ERROR(ForwardpropFromTape(
        op_type, output_tensors, backward_function_getter,
        backward_function_deleter, in_grads, absl::MakeSpan(forward_grads)));
  } else {
    TF_RETURN_IF_ERROR(
        (*forward_function)(in_grads, &forward_grads, use_batch_));
  }

    Node* node = inputs[i].oper ? &inputs[i].oper->node : nullptr;
    int idx = inputs[i].index;

    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing input ", i, " into function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),
                                    "Encountered while processing input ", i,

static void RenameFile(const TF_Filesystem* filesystem, const char* src,
                       const char* dst, TF_Status* status) {
  // If target is a directory return TF_FAILED_PRECONDITION.
  // Target might be missing, so don't error in that case.
  struct stat st;
  if (stat(dst, &st) != 0) {
    if (errno != ENOENT) {
      TF_SetStatusFromIOError(status, errno, dst);
      return;
    }
  } else if (S_ISDIR(st.st_mode)) {

#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/platform/casts.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/platform.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/profiler/lib/traceme.h"
#include "tensorflow/core/protobuf/error_codes.pb.h"
#include "tensorflow/core/public/version.h"

#if !defined(IS_MOBILE_PLATFORM)

        "//tensorflow/core/distributed_runtime:server_lib",
        "//tensorflow/core/distributed_runtime:worker_env",
        "//tensorflow/core/distributed_runtime/coordination:coordination_service_error_util",
        "//tensorflow/core/distributed_runtime/eager:eager_client",
        "//tensorflow/core/distributed_runtime/rpc:grpc_channel",
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",

        if [[ ! -f $target ]] && [[ ! -d $target ]]; then
            echo "$i" >> errors.txt
        fi
        if [[ -e errors.txt ]]; then
            echo "Broken links found:"
            cat errors.txt
            rm errors.txt
            false
        fi
    done
}

@test "No duplicate files on Windows" {
    cat <<EOF

        "tf_buffer.h",
    ],
    copts = tf_copts(),
    visibility = ["//visibility:public"],
    deps = [
        ":c_api_macros_hdrs",
        ":tf_buffer_internal",
        "//tensorflow/core/platform:errors",
        "//tensorflow/core/platform:platform_port",
        "//tensorflow/core/platform:protobuf",
        "//tensorflow/core/platform:status",
    ],
)

tf_cuda_library(
    name = "tf_buffer_internal",

  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(
        "GradientDef is not supported in reading Dataset related functions: ",
        text_proto);