namespace tf_random_access_file {

// TODO(b/139060984): Implement later

}  // namespace tf_random_access_file

// SECTION 2. Implementation for `TF_WritableFile`
// ----------------------------------------------------------------------------
namespace tf_writable_file {

// TODO(b/139060984): Implement later

}  // namespace tf_writable_file

// SECTION 3. Implementation for `TF_ReadOnlyMemoryRegion`

  if (*requested_placement == '\0') {
    TF_SetStatus(
        status, TF_INTERNAL,
        "Ops must be placed on the parallel device explicitly, or their inputs "
        "first un-packed. Got an un-placed op with an input placed on the "
        "parallel device.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, status);

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Pack 3 variable handles into one TFE_TensorHandle.
  // When remote is false, function device is placed on task0. Handle types are
  // REMOTE, REMOTE, LOCAL on task0. When remote is true, function device is
  // placed on task1, Handle types are LOCAL, REMOTE, LOCAL on task1.
  int num_replicas = 3;
  std::vector<TFE_TensorHandle*> handles = {h0, h1, h2};

  int num_retvals = 1;
  TFE_Execute(op.get(), &result_handle, &num_retvals, status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  return TensorHandlePtr(result_handle);
}

// Create and modify a variable placed on a parallel device which composes
// `first_device` and `second_device`.
void BasicTestsForTwoDevices(TFE_Context* context, const char* first_device,
                             const char* second_device) {

  LoggingDevice* dev = reinterpret_cast<LoggingDevice*>(device_info);
  if (dev->strict_scope_placement && *requested_placement == '\0') {
    TF_SetStatus(s, TF_INTERNAL,
                 "Ops must be placed on the device explicitly, or their inputs "
                 "first copied to other devices.");
    return;
  }
  TFE_Context* context = TFE_OpGetContext(original_op, s);
  if (TF_GetCode(s) != TF_OK) return;

  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  ExpectScalarEq<float>(second_components[1].get(), 9.);

  // Verify that the mirrors are placed on the component devices.
  std::string first_device = TFE_TensorHandleBackingDeviceName(
      second_components[0].get(), status.get());
  ASSERT_EQ(second_underlying_devices[0], first_device);

      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;

                        &arrived, &executed, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

  // Create a variable handle placed on the custom device.
  std::unique_ptr<TFE_Op, decltype(&TFE_DeleteOp)> op(
      TFE_NewOp(context.get(), "VarHandleOp", status.get()), TFE_DeleteOp);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());