@@ -3,6 +3,11 @@
    backported.  */
 #define _ISOMAC 1
 
+#define __GLIBC_USE(F)	__GLIBC_USE_ ## F
+
+# define __attribute_alloc_size__(params) \
+  __attribute__ ((__alloc_size__ params))
+
 #include <iostream>
 
 using namespace std;
diff --git a/sysdeps/aarch64/dl-machine.h b/sysdeps/aarch64/dl-machine.h
index 185402f..bbdeae0 100644
--- a/sysdeps/aarch64/dl-machine.h
+++ b/sysdeps/aarch64/dl-machine.h

#ifndef TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_RAM_FILE_BLOCK_CACHE_H_
#define TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_RAM_FILE_BLOCK_CACHE_H_

#include <functional>
#include <iostream>
#include <list>
#include <map>
#include <memory>
#include <string>
#include <vector>

#include "absl/base/thread_annotations.h"
#include "absl/synchronization/mutex.h"

==============================================================================*/
#include "tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h"

#include <cstring>
#include <memory>
#include <sstream>
#include <utility>

#include "absl/synchronization/mutex.h"
#include "tensorflow/c/experimental/filesystem/plugins/gcs/cleanup.h"

namespace tf_gcs_filesystem {

                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a BiasAdd: A + bias, the gradients are:
     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

    // Recover data format from forward pass for gradient.
    std::string data_format;

#include <stdio.h>

#include <fstream>
#include <string>
#include <utility>

TempFile::TempFile(const std::string& temp_file_name, std::ios::openmode mode)
    : std::fstream(temp_file_name, mode), name_(temp_file_name) {}

TempFile::TempFile(TempFile&& rhs)
    : std::fstream(std::move(rhs)), name_(std::move(rhs.name_)) {}

TempFile::~TempFile() {
  std::fstream::close();
  std::remove(name_.c_str());
}

#ifndef TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_GCS_HELPER_H_
#define TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_GCS_HELPER_H_

#include <fstream>
#include <string>

class TempFile : public std::fstream {
 public:
  // We should specify openmode each time we call TempFile.
  TempFile(const std::string& temp_file_name, std::ios::openmode mode);
  TempFile(TempFile&& rhs);

        "//tensorflow:android": [
            "//tensorflow/c/experimental/stream_executor:stream_executor_hdrs",
            "//tensorflow/core:portable_tensorflow_lib_lite",
        ],
        "//conditions:default": [
            ":tf_tensor",
            "//tensorflow/c/experimental/stream_executor",
            "//tensorflow/c/experimental/stream_executor:stream_executor_internal",
            "//tensorflow/core:framework",

    RunMetadata run_metadata_proto;
    result = session->Run(run_options_proto, input_pairs, output_tensor_names,
                          target_oper_names, &outputs, &run_metadata_proto);

    // Serialize back to upstream client, who now owns the new buffer
    if (run_metadata != nullptr) {
      status->status = MessageToBuffer(run_metadata_proto, run_metadata);
      if (!status->status.ok()) return;
    }
  } else {

    /* Given upstream grad U and a mul op A*B, the gradients are:
     *
     *    dA = U * B
     *    dB = A * U
     *
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];

    // Gradient for A
    std::string name = "Mul_Grad_A";
    TF_RETURN_IF_ERROR(Mul(ctx, upstream_grad, forward_inputs_[1],

        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_driver",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",
            "@local_xla//xla/stream_executor:cuda_platform",
        ],
    ),