lru_list_.erase(block->lru_iterator);
    lru_list_.push_front(key);
    block->lru_iterator = lru_list_.begin();
  }

  // Check for inconsistent state. If there is a block later in the same file
  // in the cache, and our current block is not block size, this likely means
  // we have inconsistent state within the cache. Note: it's possible some
  // incomplete reads may still go undetected.
  if (block->data.size() < block_size_) {

  second_block = false;
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, block_size, &out));
  EXPECT_FALSE(second_block);
  EXPECT_EQ(out.size(), file_size - block_size);
}

TEST(RamFileBlockCacheTest, Inconsistent) {
  // Tests the detection of interrupted reads leading to partially filled blocks
  // where we expected complete blocks.
  const size_t block_size = 16;

                                                     bool use_tfrt);

// Returns the context_id from the EagerContext which is used by the
// EagerService to maintain consistency between client and worker. The
// context_id is initialized with a dummy value and is later set when the worker
// is initialized (either locally or remotely). The context_id can change during

    if (gcs_file->stat_cache->Lookup(path, &stat)) {
      if (offset + read < stat.base.length) {
        TF_SetStatus(status, TF_INTERNAL,
                     absl::StrCat("File contents are inconsistent for file: ",
                                  path, " @ ", offset)
                         .c_str());
      }
      TF_VLog(2, "Successful integrity check for: %s @ %u", path.c_str(),
              offset);

/// the 3 types of files. In all other cases, there is no need to modify the
/// address of the opaque data pointer, hence the wrapper pointer is marked
/// `const`.
///
/// For consistency, the arguments on all these functions follow the same
/// pattern: first we have the opaque pointer argument ("this" above), then the
/// input arguments, then the in-out arguments (if any) and we finish the

*   Causal attention in `keras.layers.Attention` and
    `keras.layers.AdditiveAttention` is now specified in the `call()` method via
    the `use_causal_mask` argument (rather than in the constructor), for
    consistency with other layers.
*   Some files in `tensorflow/python/training` have been moved to
    `tensorflow/python/tracking` and `tensorflow/python/checkpoint`. Please

                        const int in_flight_nodes_limit)
      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(

//   In C++ bool is a keyword while in C99 bool is a macro defined
//   in stdbool.h. It is possible for the two to be inconsistent.
//   For example, neither the C99 nor the C++11 standard force a byte
//   size on the bool type, so the macro defined in stdbool.h could
//   be inconsistent with the bool keyword in C++. Thus, the use
//   of stdbool.h is avoided and unsigned char is used instead.