}

@test "Wheel conforms to upstream size limitations" {
    WHEEL_MEGABYTES=$(stat --format %s "$TF_WHEEL" | awk '{print int($1/(1024*1024))}')
    # Googlers: search for "test_tf_whl_size"
    case "$TF_WHEEL" in
        # CPU:
        *cpu*manylinux*) LARGEST_OK_SIZE=220 ;;
        # GPU:
        *manylinux*)     LARGEST_OK_SIZE=580 ;;
        # Unknown:
        *)

-	return s;
-    }
-  while (c != '\0');
-
- zero_fill:
-  do
-    *++s1 = '\0';
-  while (--n > 0);
-
-  return s;
+  size_t size = __strnlen (s2, n);
+  if (size != n)
+    memset (s1 + size, '\0', n - size);
+  return memcpy (s1, s2, size);
 }
 libc_hidden_builtin_def (strncpy)
diff --git a/sysdeps/gnu/bits/utmp.h b/sysdeps/gnu/bits/utmp.h
index aed2750..434a533 100644

  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);
  EXPECT_EQ(sizeof(float) * expected_values.size(), TF_TensorByteSize(t));
  memcpy(actual_values.get(), TF_TensorData(t), TF_TensorByteSize(t));
  TF_DeleteTensor(t);

  for (int i = 0; i < expected_values.size(); i++) {
    EXPECT_EQ(expected_values[i], actual_values[i])

      static_cast<bool>(should_enable);
}

void TF_SetXlaMinClusterSize(int size) {
  tensorflow::MarkForCompilationPassFlags* flags =
      tensorflow::GetMarkForCompilationPassFlags();
  flags->tf_xla_min_cluster_size = size;
}

TF_Buffer* TF_CreateConfig(unsigned char enable_xla_compilation,
                           unsigned char gpu_memory_allow_growth,

  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> size_op(
      TFE_NewOp(context.get(), "Size", status.get()), TFE_DeleteOp);
  auto result = parallel_device.Execute(
      context.get(), {unknown_dims_vector.get()}, "Size",
      TFE_OpGetAttrs(size_op.get()), 1, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK);
  TF_ASSERT_OK((*result)[0]->Shape(&shape));
  EXPECT_EQ(0, shape->size());
}

             AbstractContext* ctx,
             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {
    Tape tape(/*persistent=*/false);
    for (size_t i{}; i < inputs.size(); ++i) {
      tape.Watch(inputs[i]);
    }
    std::vector<AbstractTensorHandle*> temp_outputs(1);
    AbstractContextPtr tape_ctx(new TapeContext(ctx, &tape, grad_registry));
    TF_RETURN_IF_ERROR(

  Status SetAttrFunctionName(const char* attr_name, const char* value,
                             size_t length) override;
  Status SetAttrTensor(const char* attr_name,
                       AbstractTensorInterface* tensor) override;
  Status SetAttrStringList(const char* attr_name, const void* const* values,
                           const size_t* lengths, int num_values) override;
  Status SetAttrFloatList(const char* attr_name, const float* values,

TFCI_LIB_SUFFIX="-cpu-darwin-arm64"
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=1
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_BAZEL_TEST_ENABLE=1
TFCI_WHL_SIZE_LIMIT=240M
TFCI_WHL_SIZE_LIMIT_ENABLE=1

# 3.11 is the system python on our images
case $TFCI_PYTHON_VERSION in
3.11)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=0
  ;;
*)
  TFCI_MACOS_PYENV_INSTALL_ENABLE=1
  ;;

        ":not_differentiable",
        "//tensorflow/c/eager:abstract_context",
        "//tensorflow/c/eager:gradients_internal",
    ],
)

tf_cuda_cc_test(
    name = "custom_gradient_test",
    size = "small",
    srcs = [
        "custom_gradient_test.cc",
    ],
    args = ["--heap_check="],  # TODO(b/174752220): Remove
    tags = tf_cuda_tests_tags(),
    deps = [

// enabled through MarkForCompilationPassFlags.
TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled();

// Sets XLA's minimum cluster size. This has global effect.
TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than