static_cast<off_t>(offset))};
    if (r > 0) {
      dst += r;
      offset += static_cast<uint64_t>(r);
      n -= r;  // safe as 0 < r <= n so n will never underflow
      read += r;
    } else if (r == 0) {
      TF_SetStatus(status, TF_OUT_OF_RANGE, "Read fewer bytes than requested");
      break;
    } else if (errno == EINTR || errno == EAGAIN) {
      // Retry

      immediate_execution_ctx_.get(), {x.get()}, UseFunction()));
}

TEST_P(CppGradients, TestMatMulGrad) {
  // TODO(vnvo2409): Figure out why `gradient_checker` does not work very
  // well with `MatMul` and remove `TestMatMul*` in
  // `mnist_gradients_test` when done.
  GTEST_SKIP();

  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
  int64_t A_dims[] = {3, 3};

  // TODO(srbs): It seems like this is used only for performance optimization
  // and not for correctness. The only downside of keeping this 1 seems to be
  // that the gradient accumulation is unbounded and we will never
  // aggressively aggregate accumulated gradients to recover memory.
  // Revisit and fix.
  return 1;
}

// Consumes references to the tensors in the gradient_tensors list and returns

  TF_EXPECT_OK(ReadCache(&cache1, "", 0, 1, &out));
  EXPECT_EQ(calls, 1);
  // Now advance the clock one second at a time and redo the read. The call
  // count should advance every 3 seconds (i.e. every time the staleness is
  // greater than 2).
  for (int i = 1; i <= 10; i++) {
    env->SetNowSeconds(i + 1);
    TF_EXPECT_OK(ReadCache(&cache1, "", 0, 1, &out));
    EXPECT_EQ(calls, 1 + i / 3);
  }

        break;
      case FetchState::FINISHED:
        return TF_SetStatus(status, TF_OK, "");
    }
  }
  return TF_SetStatus(
      status, TF_INTERNAL,
      "Control flow should never reach the end of RamFileBlockCache::Fetch.");
}

int64_t RamFileBlockCache::Read(const std::string& filename, size_t offset,
                                size_t n, char* buffer, TF_Status* status) {
  if (n == 0) {

      : 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_(

    const TFE_OpAttrs* attributes, int expected_max_outputs,
    TF_Status* status) {
  absl::optional<std::vector<MaybeParallelTensorOwned>> result;
  // TODO(allenl): We should remove "TPU" from these op names at the very least,
  // or consider other ways of packing/unpacking parallel tensors.
  if (operation_name == std::string("TPUReplicatedInput")) {
    // Special-cased operation for packing per-device tensors into one parallel
    // tensor.