if (!status->status.ok()) return;
      input_tensors_vector[i] = &input_tensor;
    }
  }

  // Create an inference context with dummy values, which will be updated later.
  InferenceContext c(TF_GRAPH_DEF_VERSION, node_def, op_reg_data->op_def,
                     std::vector<ShapeHandle>(num_inputs), input_tensors_vector,
                     {},

    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.

  TF_EXPECT_OK(ReadCache(&cache, "", block_size, block_size, &out));
  EXPECT_EQ(out.size(), 1);
  // Now read the first block; this should yield an INTERNAL error because we
  // had already cached a partial block at a later position.
  Status status = ReadCache(&cache, "", 0, block_size, &out);
  EXPECT_EQ(status.code(), error::INTERNAL);
}

TEST(RamFileBlockCacheTest, LRU) {
  const size_t block_size = 16;