typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.0.0.dev
tb-nightly ~= 2.17.0.a

# Test dependencies
grpcio >= 1.24.3, < 2.0

                              TracingTensorHandle**) = 0;

  // Finalize this context and make a function out of it. The context is in a
  // invalid state after this call and must be destroyed.
  virtual Status Finalize(OutputList* outputs, AbstractFunction**) = 0;

  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {

    if (op_) {
      return errors::FailedPrecondition(
          "SetOpName called on already built op.");
    }
    if (op_type_.empty()) {
      return errors::FailedPrecondition(
          "GraphOperation::Reset must be called before calling SetOpName.");
    }
    // TODO(b/145674566): We use Graph::NewName to get a unique name here but
    // this may not be consistent with python's naming policy.
    mutex_lock l(g_->mu);

  AbstractTensorHandle* GetHandle() const;

 private:
  AbstractTensorHandle* handle_;
};

// A tracing/immediate-execution agnostic tape.
//
// Gradient functions defined for this tape must support handling null incoming
// gradients.
class Tape : protected eager::GradientTape<AbstractTensorHandle,
                                           GradientFunction, TapeTensor> {
 public:

  // Distributed runtime related functions.
  //===--------------------------------------------------------------------===//
#if !defined(IS_MOBILE_PLATFORM)
  // Set up a multi-client distributed execution environment. Must be called on
  // all tasks in the cluster.
  // This call internally coordinates with other tasks to initialize the eager
  // context and TF server for multi-client execution.

              bool expect_failure = false) {
    DefineT(num_opers, opers, ToOutput(inputs), ToOutput(outputs), output_names,
            expect_failure);
  }

  // Caller must delete[] the returned value
  static const char** ToArray(const std::vector<string>& strs) {
    const char** ptr = nullptr;
    if (!strs.empty()) {
      ptr = new const char*[strs.size()];

                          GcsFileStat* stat, TF_Status* status) {
  if (object.empty())
    return TF_SetStatus(
        status, TF_INVALID_ARGUMENT,
        absl::StrCat("'object' must be a non-empty string. (File: ", path, ")")
            .c_str());
  TF_SetStatus(status, TF_OK, "");
  gcs_file->stat_cache->LookupOrCompute(
      path, stat,

  TensorHandlePtr cpu_value(FloatTensorHandle(3., status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Copying on to a parallel device must be explicit.
  TensorHandlePtr failed_copy_on_result(TFE_TensorHandleCopyToDevice(
      cpu_value.get(), context.get(), device_name, status.get()));
  EXPECT_EQ(TF_GetCode(status.get()), TF_UNIMPLEMENTED);

build:dbg --per_file_copt=+tensorflow/core/kernels.*@-g0
# for now, disable arm_neon. see: https://github.com/tensorflow/tensorflow/issues/33360
build:dbg --cxxopt -DTF_LITE_DISABLE_X86_NEON
# AWS SDK must be compiled in release mode. see: https://github.com/tensorflow/tensorflow/issues/37498
build:dbg --copt -DDEBUG_BUILD

# Config to build TF TPU
build:tpu --define=with_tpu_support=true

index c9bf593..f461fc1 100644
--- a/string/bits/string2.h
+++ b/string/bits/string2.h
@@ -47,29 +47,7 @@
 #endif
 
 #if _STRING_ARCH_unaligned
-/* If we can do unaligned memory accesses we must know the endianess.  */
-# include <endian.h>
 # include <bits/types.h>
-
-# if __BYTE_ORDER == __LITTLE_ENDIAN
-#  define __STRING2_SMALL_GET16(src, idx) \