// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.
// The effect is bounding the memory held by inflight TensorHandles that are

  /// The size of the blocks stored in the LRU cache, as well as the size of the
  /// reads from the underlying filesystem.
  const size_t block_size_;
  /// The maximum number of bytes (sum of block sizes) allowed in the LRU cache.
  const size_t max_bytes_;
  /// The maximum staleness of any block in the LRU cache, in seconds.
  const uint64_t max_staleness_;
  /// The callback to read a block from the underlying filesystem.

your report:

  - A descriptive title
  - Your name and affiliation (if any).
  - A description of the technical details of the vulnerabilities.
  - A minimal example of the vulnerability. It is very important to let us know
    how we can reproduce your findings. For memory corruption triggerable in
    TensorFlow models, please demonstrate an exploit against one of Alphabet's

#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

// Checks that all schemes provided by a plugin are valid.
// TODO(b/139060984): More validation could be done here, based on supported
// charset, maximum length, etc. Punting it for later.
static Status ValidateScheme(const char* scheme) {
  if (scheme == nullptr)
    return errors::InvalidArgument(
        "Attempted to register filesystem with `nullptr` URI scheme");

// blocks read from GCS. Specified in MB.
constexpr char kMaxCacheSize[] = "GCS_READ_CACHE_MAX_SIZE_MB";
constexpr size_t kDefaultMaxCacheSize = 0;
// The environment variable that overrides the maximum staleness of cached file
// contents. Once any block of a file reaches this staleness, all cached blocks
// will be evicted on the next read.
constexpr char kMaxStaleness[] = "GCS_READ_CACHE_MAX_STALENESS";

    default_cc_opt_flags = '/arch:AVX'
  else:
    # On all other platforms, no longer use `-march=native` as this can result
    # in instructions that are too modern being generated. Users that want
    # maximum performance should compile TF in their environment and can pass
    # `-march=native` there.
    # See https://github.com/tensorflow/tensorflow/issues/45744 and duplicates
    default_cc_opt_flags = '-Wno-sign-compare'