std::list<size_t> calls;
  auto fetcher = [&calls, block_size](const string& filename, size_t offset,
                                      size_t n, char* buffer,
                                      TF_Status* status) -> int64_t {
    EXPECT_EQ(n, block_size);
    EXPECT_FALSE(calls.empty()) << "at offset = " << offset;
    if (!calls.empty()) {
      EXPECT_EQ(offset, calls.front());
      calls.pop_front();
    }

TEST(ExpiringLRUCacheTest, LookupOrCompute) {
  // max_age of 0 means we should always compute.
  uint64 num_compute_calls = 0;
  tf_gcs_filesystem::ExpiringLRUCache<int>::ComputeFunc compute_func =
      [&num_compute_calls](const string& key, int* value, TF_Status* status) {
        *value = num_compute_calls;
        num_compute_calls++;
        return TF_SetStatus(status, TF_OK, "");
      };

// Wraps a pointer to a context implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying context object. Instead, call
// TFE_DeleteContext who calls Release() on the context pointer and deletes
// the TFE_Context structure.
typedef struct TFE_Context TFE_Context;

namespace tensorflow {

#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/file_system_helper.h"
#include "tsl/platform/errors.h"

// TODO(b/139060984): After all filesystems are converted, all calls to
// methods from `FileSystem` will have to be replaced to calls to private
// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

namespace tensorflow {

using UniquePtrTo_TF_Status =

// but there's rarely a reason to do that.
//
// You can call 'release()' on a Cleanup object to cancel the cleanup.

#ifndef TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_CLEANUP_H_
#define TENSORFLOW_C_EXPERIMENTAL_FILESYSTEM_PLUGINS_GCS_CLEANUP_H_

#include <type_traits>
#include <utility>

namespace tf_gcs_filesystem {

// A move-only RAII object that calls a stored cleanup functor when

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Setup a session and a partial run handle.  The partial run will allow
  // computation of A + 2 + B in two phases (calls to TF_SessionPRun):
  // 1. Feed A and get (A+2)
  // 2. Feed B and get (A+2)+B
  TF_SessionOptions* opts = TF_NewSessionOptions();
  TF_Session* sess = TF_NewSession(graph, opts, s);
  TF_DeleteSessionOptions(opts);

// Wraps a pointer to an operation implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying operation object. Instead, call
// TFE_DeleteOp who calls Release() on the operation pointer and deletes
// the TFE_Op structure.
typedef struct TFE_Op TFE_Op;

namespace tensorflow {

DEFINE_CONVERSION_FUNCTIONS(tensorflow::ImmediateExecutionOperation, TFE_Op);

// Wraps a pointer to a tensor handle implementation.
//
// WARNING: Since the underlying object could be ref-counted a user of this
// interface cannot destruct the underlying handle object. Instead, call
// TFE_DeleteTensorHandle who calls Release() on the handle pointer and deletes
// the TFE_TensorHandle structure.
typedef struct TFE_TensorHandle TFE_TensorHandle;

namespace tensorflow {

  tensorflow::mutex mu TF_ACQUIRED_AFTER(TF_Graph::mu);
  int last_num_graph_nodes;

  // If true, TF_SessionRun and similar methods will call
  // ExtendSessionGraphHelper before running the graph (this is the default
  // public behavior). Can be set to false if the caller needs to call
  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {

                                   absl::Span<AbstractTensorHandle* const>,
                                   absl::Span<AbstractTensorHandle*>)>;

// Runs `model` maybe wrapped in a function call op. This can be thought as
// being equivalent to the following python code.
//
// if use_function:
//   outputs = tf.function(model)(inputs)
// else:
//   outputs = model(inputs)