typedef struct TF_Filesystem_Option_Value {
  int type_tag;    // type of values in the values union
  int num_values;  // number of values
  TF_Filesystem_Option_Value_Union*
      values;  // owned (plugins must make a copy if storing this)
} TF_Filesystem_Option_Value;

typedef enum TF_Filesystem_Option_Type {
  TF_Filesystem_Option_Type_Int = 0,
  TF_Filesystem_Option_Type_Real,
  TF_Filesystem_Option_Type_Buffer,

#include <stdlib.h>
#include <string.h>

#include <variant>

#include "absl/strings/numbers.h"
#include "absl/strings/str_cat.h"
#include "absl/types/variant.h"
#include "google/cloud/storage/client.h"
#include "tensorflow/c/env.h"
#include "tensorflow/c/experimental/filesystem/plugins/gcs/gcs_helper.h"
#include "tensorflow/c/logging.h"
#include "tensorflow/c/tf_status.h"

    TF_Status* status);

// Information about the shape of a Tensor and its type.
struct TF_ShapeAndType {
  // Number of dimensions. -1 indicates unknown rank.
  int num_dims;
  // Array of dimensions. -1 indicates unknown dim.
  int64_t* dims;
  // The data type. May be 0 to denote unknown type.
  TF_DataType dtype;
};

typedef struct TF_ShapeAndType TF_ShapeAndType;

                     TF_ShapeAndTypeList* input_tensors_as_shapes,
                     TF_ShapeAndTypeList** input_resource_shapes_and_types,
                     TF_ShapeAndTypeList** output_shapes,
                     TF_ShapeAndTypeList*** output_resource_shapes_and_types,
                     TF_Status* status) {
  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;

        "//tensorflow/c/experimental/filesystem:filesystem_interface",
        "@com_github_googlecloudplatform_google_cloud_cpp//:storage_client",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "gcs_helper",
    srcs = ["gcs_helper.cc"],
    hdrs = ["gcs_helper.h"],
    linkstatic = 1,
    deps = [
        "//tensorflow/c:env",
    ],
)

        "//tensorflow/c/experimental/ops:math_ops",
        "//tensorflow/c/experimental/ops:nn_ops",
        "//tensorflow/core/lib/llvm_rtti",
        "//tensorflow/core/platform:errors",
        "@com_google_absl//absl/types:span",
    ],
)

cc_library(
    name = "not_differentiable",
    srcs = ["not_differentiable.cc"],
    hdrs = [
        "not_differentiable.h",
    ],
    visibility = [

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/gtl/cleanup.h"
#include "tensorflow/core/lib/gtl/flatmap.h"
#include "tensorflow/core/lib/gtl/flatset.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace eager {

-      | ((const unsigned char *) (const char *) (src))[idx + 3])
-# endif
 #else
 /* These are a few types we need for the optimizations if we cannot
    use unaligned memory accesses.  */
@@ -94,148 +72,11 @@ __STRING2_COPY_TYPE (8);
 
 /* Set N bytes of S to C.  */
 #if !defined _HAVE_STRING_ARCH_memset
-# if !__GNUC_PREREQ (3, 0)
-#  if _STRING_ARCH_unaligned

build:release_linux_base --config=avx_linux

# Enable support for all targets
build:release_base --config=cpu_cross

# Disable clang extension that rejects type definitions within offsetof.
# This was added in clang-16 by https://reviews.llvm.org/D133574.
# Can be removed once upb is updated, since a type definition is used within
# offset of in the current version of ubp.

                 "The allocated output buffer was too small.");
    return;
  }

  for (int i = 0; i < typed_outputs.size(); ++i) {
    MaybeParallelTensorOwned typed_output(std::move(typed_outputs[i]));
    if (absl::holds_alternative<TensorHandlePtr>(typed_output)) {
      outputs[i] = absl::get<TensorHandlePtr>(typed_output).release();
    } else {
      outputs[i] = ParallelTensorToTensorHandle(