// make sure there is no information loss.
  if (proto_len > std::numeric_limits<int>::max()) {
    status->status = InvalidArgument(
        "proto_len (", proto_len,
        " bytes) is too large to be parsed by the protocol buffer library");
    return;
  }
  TensorShapeProto shape;
  if (shape.ParseFromArray(proto, static_cast<int>(proto_len))) {
    desc->node_builder.Attr(attr_name, shape);

  TFE_DeleteContextOptions(opts);

  TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status);
  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  // Use large matrices so that RPCs don't return before we get a chance
  // to call TFE_DeleteContext.
  TFE_TensorHandle* h0_task0 = TestMatrixTensorHandle100x100(ctx);
  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle100x100(ctx);

  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a
  // CPU tensor to GPU).
  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph
  // functions, and their send/recvs corountines).

  TF_TString* data = static_cast<TF_TString*>(TF_TensorData(t));
  for (int i = 0; i < batch_size; ++i) {
    TF_TString_Init(&data[i]);
    // The following input string length is large enough to make sure that
    // copy to tstring in large mode.
    std::string source =
        "This is the " + std::to_string(i + 1) + "th. data element\n";
    TF_TString_Copy(&data[i], source.c_str(), source.length());
  }

    EXPECT_EQ(got_n, want_n);
    calls++;
    memset(buffer, 'x', got_n);
    TF_SetStatus(status, TF_OK, "");
    return got_n;
  };
  // If block_size, max_bytes, or both are zero, or want_n is larger than
  // max_bytes the cache is a pass-through.
  tf_gcs_filesystem::RamFileBlockCache cache1(1, 0, 0, fetcher);
  tf_gcs_filesystem::RamFileBlockCache cache2(0, 1, 0, fetcher);