// Run a function containing a MatMul op and check its output.
// If heavy_load_on_streaming_rpc is true, send some rpc requests before the one
// which creates a remote input, to simulate a scenario that the remote input
// is not ready when we start running an op or a function.
void TestRemoteExecuteSilentCopies(bool async, bool remote, bool func,
                                   bool heavy_load_on_streaming_rpc,

                                    /*remote_func_outputs=*/true);
}
TEST(CAPI, RemoteExecuteSilentCopiesLocalAsyncFuncOrdering) {
  // A remote input may be not ready when we start running a function. Test that
  // the function execution should wait until the remote input is ready.
  TestRemoteExecuteSilentCopiesFunc(/*async=*/true, /*remote=*/false,
                                    /*heavy_load_on_streaming_rpc=*/true);
}

# TODO(b/304751256): Adjust the numpy pin to a single version, when ready
numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras

//
// If async execution is enabled, the call may simply enqueue the execution
// and return "non-ready" handles in `retvals`. Note that any handles contained
// in 'op' should not be mutated till the kernel execution actually finishes.
//
// For sync execution, if any of the inputs to `op` are not ready, this call
// will block till they become ready and then return when the kernel execution
// is done.

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

  std::vector<char> out;

  // First read.
  EXPECT_TRUE(cache.ValidateAndUpdateFileSignature(filename, 123));
  TF_EXPECT_OK(ReadCache(&cache, filename, 0, 16, &out));
  EXPECT_EQ(calls, 1);

  // Second read. Hit cache.
  EXPECT_TRUE(cache.ValidateAndUpdateFileSignature(filename, 123));
  TF_EXPECT_OK(ReadCache(&cache, filename, 0, 16, &out));
  EXPECT_EQ(calls, 1);

  // Third read. File signatures are different.

}

int64_t RamFileBlockCache::Read(const std::string& filename, size_t offset,
                                size_t n, char* buffer, TF_Status* status) {
  if (n == 0) {
    TF_SetStatus(status, TF_OK, "");
    return 0;
  }
  if (!IsCacheEnabled() || (n > max_bytes_)) {
    // The cache is effectively disabled, so we pass the read through to the
    // fetcher without breaking it up into blocks.

    size_t chunk = size - offset;
    if (chunk > kPosixCopyFileBufferSize) chunk = kPosixCopyFileBufferSize;

    rc = read(src_fd, buffer.get(), chunk);
    if (rc < 0) return -1;

    int total_write = 0;
    int total_read = rc;
    while (total_write < total_read && rc > 0) {
      rc = write(dst_fd, buffer.get() + total_write, chunk - total_write);
      if (rc < 0) return -1;

      total_write += rc;

  tf_gcs_filesystem::ExpiringLRUCache<int> cache2(2, 4);
  num_compute_calls = 0;
  value = -1;

  // Read our first value
  cache2.LookupOrCompute("a", &value, compute_func, &status);
  TF_EXPECT_OK(status.status);
  EXPECT_EQ(value, 0);
  EXPECT_EQ(num_compute_calls, 1);
  // Re-read, exepct no additional function compute_func calls.
  cache2.LookupOrCompute("a", &value, compute_func, &status);

}

Status ModularRandomAccessFile::Read(uint64 offset, size_t n,
                                     StringPiece* result, char* scratch) const {
  if (ops_->read == nullptr)
    return errors::Unimplemented(
        tensorflow::strings::StrCat("Read() not implemented for ", filename_));

  UniquePtrTo_TF_Status plugin_status(TF_NewStatus(), TF_DeleteStatus);
  int64_t read =