TEST(RamFileBlockCacheTest, ParallelReads) {
  // This fetcher won't respond until either `callers` threads are calling it
  // concurrently (at which point it will respond with success to all callers),
  // or 10 seconds have elapsed (at which point it will respond with an error).
  const int callers = 4;
  BlockingCounter counter(callers);
  auto fetcher = [&counter](const string& filename, size_t offset, size_t n,

    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.
  if (block->data.size() < block_size_) {
    Key fmax = std::make_pair(key.first, std::numeric_limits<size_t>::max());

  TFE_TensorHandle* h1_task0 = TestMatrixTensorHandle(ctx);
  std::vector<TFE_TensorHandle*> handles_task0;
  if (heavy_load_on_streaming_rpc) {
    // Send 50 tensor copy requests to simulate that there have been some RPC
    // requests been enqueued.
    for (int i = 0; i < 50; ++i) {
      handles_task0.push_back(TestMatrixTensorHandle(ctx));
    }
  }

    int num_retvals = 1;
    TFE_Execute(matmul, &retvals[0], &num_retvals, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

    // The CPU handle should have been copied and have a mirror on the GPU
    ASSERT_TRUE(cpu_arg->HasLocalMirror(gpu_device));

    TFE_DeleteOp(matmul);
    TFE_DeleteTensorHandle(retvals[0]);
    TFE_DeleteTensorHandle(hgpu);
  }

  PartialTensorShape shape;
  TF_RETURN_IF_ERROR(inputs[0]->Shape(&shape));
  if (shape.dims() != 0) {
    return errors::InvalidArgument(
        "Tensor expected to have scalar shape found rank: ", shape.dims());
  }
  return absl::OkStatus();
}

TEST_P(UnifiedAPI, TestTensorShapeScalar) {
  if (UseFunction() && UseMlir()) {
    // TODO(b/173074167): Remove this.

    variable->Assign(context, initial_value.get(), status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  }

  // Read from the variable and verify that we have a parallel tensor.
  {
    TensorHandlePtr read = variable->Read(context, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    std::array<TensorHandlePtr, 2> components;

        "operations on them have been supplied.");

  if ((ops->filesystem_ops->new_writable_file != nullptr ||
       ops->filesystem_ops->new_appendable_file != nullptr) &&
      ops->writable_file_ops == nullptr)
    return errors::FailedPrecondition(
        "Filesystem allows creation of writable files but no "
        "operations on them have been supplied.");

#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 =

  tensorflow::AsyncValueTensor* av_tensor =
      tensorflow::AsyncValueTensor::FromTensor(tensor);
  if (av_tensor == nullptr || av_tensor->GetBuffer() == nullptr) {
    return absl::InternalError("Input tensor does not have PjRtBuffer.");
  }
  auto* c_api_buffer =
      dynamic_cast<xla::PjRtCApiBuffer*>(av_tensor->GetBuffer().get());
  if (c_api_buffer == nullptr) {
    return absl::InternalError(

  server_def_0.mutable_default_session_config()->set_isolate_session_state(
      false);
  tensorflow::ServerDef server_def_1 =
      ReplaceTaskInServerDef(server_def_0, /*task_index=*/0);

  // These server defs have task index set to 0.
  string serialized_server_def_0 = server_def_0.SerializeAsString();
  string serialized_server_def_1 = server_def_1.SerializeAsString();

  // Create two worker tasks.