# On linux, we dynamically link small amount of kernels
build:linux --config=dynamic_kernels

# Make sure to include as little of windows.h as possible
build:windows --copt=-DWIN32_LEAN_AND_MEAN
build:windows --host_copt=-DWIN32_LEAN_AND_MEAN
build:windows --copt=-DNOGDI
build:windows --host_copt=-DNOGDI

# MSVC (Windows): Standards-conformant preprocessor mode

}

namespace {

struct SomeThreadData {
  ::tensorflow::mutex mu;
  bool did_work = false;
};

void SomeThreadFunc(void* data) {
  auto* real_data = static_cast<SomeThreadData*>(data);
  ::tensorflow::mutex_lock l(real_data->mu);
  real_data->did_work = true;
}

}  // namespace

TEST(TestEnv, TestThreads) {
  TF_ThreadOptions options;
  TF_DefaultThreadOptions(&options);

We'd love to accept your patches! Before we can take them, we have to jump a couple of legal hurdles.

Please fill out either the individual or corporate Contributor License Agreement (CLA).

  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](https://code.google.com/legal/individual-cla-v1.0.html).

  }

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace gradients {
namespace {

// TODO(b/172558015): Using the pointer address as the identifier for the tensor
// may lead to collisions. Introduce another way to get a unique id for this
// tensor.
int64_t ToId(const AbstractTensorHandle* t) {
  return static_cast<int64_t>(reinterpret_cast<uintptr_t>(t));
}

research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running
and leak or tamper with applications from other users. Please report
vulnerabilities to the vendor of the affected hardware accelerator.

// leader and become accessible to all workers in the cluster.
// Currently, a config key can only be set with one value, and subsequently
// setting the same key will lead to errors.
//
// Note that the key-values are only expected to be used for cluster
// configuration data, and should not be used for storing a large amount of data
// or being accessed very frequently.

        convolution algorithms, a regression that was introduced in v2.5. Note
        that nondeterministic out-of-memory events while selecting algorithms
        could still lead to nondeterminism, although this is very unlikely. This
        additional, unlikely source will be eliminated in a later version.
    *   Add deterministic GPU implementations of:

  auto gcs_file = static_cast<GCSFile*>(filesystem->plugin_filesystem);
  char* temp_file_name_c_str = TF_GetTempFileName("");
  std::string temp_file_name(temp_file_name_c_str);  // To prevent memory-leak
  free(temp_file_name_c_str);

  if (!gcs_file->compose) {
    auto gcs_status =
        gcs_file->gcs_client.DownloadToFile(bucket, object, temp_file_name);
    TF_SetStatusFromGCSStatus(gcs_status, status);

    linkstatic = False,
    tags = [
        "manual",
        "nobuilder",
        "notap",
    ],
    visibility = ["//visibility:public"],
    deps = [":windows_filesystem_impl"],
)

# The real implementation of the filesystem.
cc_library(
    name = "windows_filesystem_impl",
    srcs = ["windows_filesystem.cc"],
    copts = get_win_copts(),
    tags = [
        "manual",
        "nobuilder",