FileSystem* fs = nullptr;
  Status s = env_->GetFileSystemForFile(generic_path, &fs);
  if (fs == nullptr || !s.ok())
    GTEST_SKIP() << "No filesystem registered: " << s;

  // First, test some interesting corner cases concerning empty URIs
  if (GetParam().empty()) {
    EXPECT_EQ(fs->TranslateName(""), "");
    EXPECT_EQ(fs->TranslateName("/"), "/");
    EXPECT_EQ(fs->TranslateName("//"), "/");

#   variables in between script executions. To forcibly remove the
#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you

// Creates a new TensorHandle from memory residing in the physical device
// device_name. Takes ownership of the memory, and will call deleter to release
// it after TF no longer needs it or in case of error.
//
// Custom devices must use TFE_NewCustomDeviceTensorHandle instead.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_NewTensorHandleFromDeviceMemory(

    for the required packages. Alternatively, use the said
    [tensorflow/build Docker images](https://hub.docker.com/r/tensorflow/build)
    (`tensorflow/tensorflow:devel` and `tensorflow/tensorflow:devel-gpu` are no
    longer supported for) development. Use TF SIG Build Dockerfiles in
    development to avoid installing the packages directly on your system (in
    which case remember to change the directory from `/root` to `/tensorflow`

                                                  TF_Function* function,
                                                  TF_Status* status);

// Removes a function from the context. Once removed, you can no longer
// TFE_Execute it or TFE_Execute any TFE_Op which has it as an attribute or any
// other function which calls it as an attribute.
TF_CAPI_EXPORT extern void TFE_ContextRemoveFunction(TFE_Context* ctx,

///     break ABI compatibility and cause all plugins to be recompiled, these
///     type of changes should be extremely rare.
///
/// Next section will detail this as well as some corner cases that are out of
/// scope for now.

// LINT.IfChange
typedef struct TF_RandomAccessFileOps {
  /// Releases resources associated with `*file`.
  ///

        `DynamicLossScale` will no longer affect the weights of the
        LossScaleOptimizer, and vice versa.
    *   The global policy can no longer be set to a non-floating point policy in
        `tf.keras.mixed_precision.experimental.set_policy`
    *   In `Layer.call`, `AutoCastVariable`s will no longer be casted within

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --enable_platform_specific_config

# Enable XLA support by default.
build --define=with_xla_support=true

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --config=short_logs

# TODO(mihaimaruseac): Document this option or remove if no longer needed
build --config=v2

# Disable AWS/HDFS support by default

  gpu_options->set_allow_growth(gpu_memory_allow_growth);

  (*config.mutable_device_count())["CPU"] = num_cpu_devices;

  // TODO(b/113217601): This is needed for EagerContext::runner_ to use a
  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the

    # gcc on ppc64le does not support -march, use mcpu instead
    default_cc_opt_flags = '-mcpu=native'
  elif is_windows():
    default_cc_opt_flags = '/arch:AVX'
  else:
    # On all other platforms, no longer use `-march=native` as this can result
    # in instructions that are too modern being generated. Users that want
    # maximum performance should compile TF in their environment and can pass
    # `-march=native` there.