}

  char file_name[100];
  time_t t = time(NULL);
  snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t);

  size_t length = 2 + strlen(path) + strlen(file_name);
  char* full_path = malloc(length);
  snprintf(full_path, length, "%s/%s", path, file_name);

  TF_WritableFileHandle* h;
  TF_Status* status = TF_NewStatus();
  TF_NewWritableFile(full_path, &h, status);
  if (TF_GetCode(status) != TF_OK) {

}

const void TFE_MonitoringStringGaugeCellValue(
    TFE_MonitoringStringGaugeCell* cell, TF_Buffer* buf) {
  tensorflow::string value = cell->cell.value();
  void* data = tensorflow::port::Malloc(value.length());
  value.copy(static_cast<char*>(data), value.length(), 0);
  buf->data = data;
  buf->length = value.length();
  buf->data_deallocator = [](void* data, size_t length) {
    tensorflow::port::Free(data);

	func C.GoBytes(unsafe.Pointer, C.int) []byte

As a special case, C.malloc does not call the C library malloc directly
but instead calls a Go helper function that wraps the C library malloc
but guarantees never to return nil. If C's malloc indicates out of memory,
the helper function crashes the program, like when Go itself runs out
of memory. Because C.malloc cannot fail, it has no two-result form
that returns errno.

  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

char* TF_FunctionDebugString(TF_Function* func, size_t* len) {
  const auto& debug_str = DebugString(func->record->fdef());
  *len = debug_str.size();
  char* ret = static_cast<char*>(malloc(*len + 1));
  memcpy(ret, debug_str.c_str(), *len + 1);
  return ret;
}

		Endpoint: addr,
		Uptime:   UTCNow().Unix() - globalBootTime.Unix(),
		Version:  Version,
		CommitID: CommitID,
		Network:  network,
		MemStats: madmin.MemStats{
			Alloc:      memstats.Alloc,
			TotalAlloc: memstats.TotalAlloc,
			Mallocs:    memstats.Mallocs,
			Frees:      memstats.Frees,
			HeapAlloc:  memstats.HeapAlloc,
		},
		GoMaxProcs:     runtime.GOMAXPROCS(0),
		NumCPU:         runtime.NumCPU(),

<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
  <component name="KotlinCompilerSettings">
    <option name="additionalArguments" value="-version -Xallow-kotlin-package -Xskip-metadata-version-check" />
  </component>
  <component name="KotlinJpsPluginSettings">
    <option name="version" value="2.0.20-dev-2651" />
  </component>

    *   Deprecate, and replace by tf.data.experimental.
*   Other:
    *   Instead of jemalloc, revert back to using system malloc since it
        simplifies build and has comparable performance.
    *   Remove integer types from `tf.nn.softplus` and `tf.nn.softsign` OpDefs.
        This is a bugfix; these ops were never meant to support integers.
    *   Allow subslicing Tensors with a single dimension.

                            "application/x-www-form-urlencoded": {
                                "schema": IsDict(
                                    {
                                        "allOf": [
                                            {
                                                "$ref": "#/components/schemas/Body_read_items_items__post"
                                            }

            lt=lt,
            le=le,
            min_length=min_length,
            max_length=max_length,
            discriminator=discriminator,
            multiple_of=multiple_of,
            allow_nan=allow_inf_nan,
            max_digits=max_digits,
            decimal_places=decimal_places,
            **extra,
        )
        if examples is not None:
            kwargs["examples"] = examples

            """
        ),
    ] = _Unset,
    allow_inf_nan: Annotated[
        Union[bool, None],
        Doc(
            """
            Allow `inf`, `-inf`, `nan`. Only applicable to numbers.
            """
        ),
    ] = _Unset,
    max_digits: Annotated[
        Union[int, None],
        Doc(
            """
            Maximum number of allow digits for strings.
            """
        ),