tensorflow::ImmediateExecutionTensorHandle* unwrapped_handle =
        tensorflow::unwrap(handles[i]);
    if (tensorflow::CustomDeviceTensorHandle::classof(unwrapped_handle)) {
      // One of the inputs we're trying to pack is on a custom device. We'll let
      // the first custom device we see handle all of the packing.
      auto* custom_device_handle =
          tensorflow::down_cast<tensorflow::CustomDeviceTensorHandle*>(

  tracing::TracingTensorHandle* handle = nullptr;
  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);
  }
  return absl::OkStatus();
}

*   Updates `libjpeg-turbo` to `2.0.5` to handle
    [CVE-2020-13790](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-13790).
*   Updates `junit` to `4.13.1` to handle
    [CVE-2020-15250](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15250).
*   Updates `PCRE` to `8.44` to handle
    [CVE-2019-20838](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2019-20838)
    and

limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/gradients/math_grad.h"

#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/gradients.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/experimental/ops/nn_ops.h"

using std::vector;

        "@local_xla//xla/service:computation_placer",
        "//tensorflow/core",
        "//tensorflow/core/common_runtime/eager:context",
        "//tensorflow/core/common_runtime/eager:tensor_handle",
        "//tensorflow/core/config:flag_defs",
        "//tensorflow/core/config:flags",
        "//tensorflow/core/data/service:dispatcher_client",
        "//tensorflow/core/data/service:grpc_util",

    if (it != loaded_libs->end()) {
      lib_handle->lib_handle = it->second;
    } else {
      status->status =
          env->LoadDynamicLibrary(library_filename, &lib_handle->lib_handle);
      if (status->status.ok()) {
        TF_CHECK_OK(
            tensorflow::RegisterPluggableDevicePlugin(lib_handle->lib_handle));
      } else {
        delete lib_handle;
        return nullptr;
      }
    }

# Workaround for networking (b/112379377) was closed as won't fix for MTU setting.
# Remaining lines handle making the metadata server on the VM accessible inside docker.
RUN Get-NetAdapter | Where-Object Name -like "*Ethernet*" | ForEach-Object { \
        & netsh interface ipv4 set subinterface $_.InterfaceIndex mtu=1460 store=persistent }; \

        "nn_grad.h",
    ],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/c/eager:abstract_tensor_handle",
        "//tensorflow/c/eager:gradients_internal",
        "//tensorflow/c/eager:immediate_execution_context",
        "//tensorflow/c/eager:immediate_execution_tensor_handle",
        "//tensorflow/c/experimental/ops:array_ops",
        "//tensorflow/c/experimental/ops:math_ops",

// SECTION 2. Implementation for `TF_WritableFile`
// ----------------------------------------------------------------------------
namespace tf_writable_file {

typedef struct PosixFile {
  const char* filename;
  FILE* handle;
} PosixFile;

static void Cleanup(TF_WritableFile* file) {
  auto posix_file = static_cast<PosixFile*>(file->plugin_file);
  plugin_memory_free(const_cast<char*>(posix_file->filename));
  delete posix_file;
}

-}
-#endif
-
-
-/* Find the first occurrence of NEEDLE in HAYSTACK.  Newer gcc versions
-   do this itself.  */
-#if !defined _HAVE_STRING_ARCH_strstr && !__GNUC_PREREQ (2, 97)
-# define strstr(haystack, needle) \
-  (__extension__ (__builtin_constant_p (needle) && __string2_1bptr_p (needle) \
-		  ? (((const char *) (needle))[0] == '\0'		      \
-		     ? (char *) (size_t) (haystack)			      \