TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));
  if (!tracing_ctx) {
    tsl::Set_TF_Status_from_Status(
        s, tensorflow::errors::InvalidArgument(
               "TF_AddFunctionParameter must be called on a TracingContext."));
    return nullptr;
  }
  tensorflow::PartialTensorShape partial_shape;
  if (shape.num_dims != -1) {
    DCHECK(shape.dim_sizes != nullptr);

      "ApiDefMap is not supported on mobile.");
#else
  mutex_lock l(api_def_map->lock);
  if (api_def_map->update_docs_called) {
    status->status = FailedPrecondition(
        "TF_ApiDefMapPut cannot be called after TF_ApiDefMapGet has been "
        "called.");
    return;
  }
  string api_def_text(text, text_len);
  status->status = api_def_map->api_def_map.LoadApiDef(api_def_text);

  // scheduled on that same threadpool, causing a deadlock in cases where the
  // 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

          TF_TensorDefaultAlignment(), num_bytes));
  int64_t dims[] = {2, 3};
  bool deallocator_called = false;
  TF_Tensor* t = TF_NewTensor(TF_FLOAT, dims, 2, values, num_bytes,
                              &Deallocator, &deallocator_called);
  EXPECT_FALSE(deallocator_called);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(t));
  EXPECT_EQ(2, TF_NumDims(t));
  EXPECT_EQ(dims[0], TF_Dim(t, 0));

    return;
  }

  struct stat st;
  fstat(fd, &st);
  if (S_ISDIR(st.st_mode)) {
    TF_SetStatus(status, TF_FAILED_PRECONDITION, "path is a directory");
  } else {
    const void* address =
        mmap(nullptr, st.st_size, PROT_READ, MAP_PRIVATE, fd, 0);
    if (address == MAP_FAILED) {
      TF_SetStatusFromIOError(status, errno, path);
    } else {
      region->plugin_memory_region =

  // finished, if any.
  //
  // `cancellation_manager` must live until after `Join` finishes and pending
  // `is_async` operations finish. In addition to allowing the caller to cancel
  // the operation, its `StartCancel` method will be called if op execution
  // fails on any device in order to cancel the others.
  void StartExecute(TFE_Context* context, const char* operation_name,

#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 =

    if (!enabled_) {
      return absl::OkStatus();
    }
    if (first_call_) {
      first_call_ = false;
      return absl::OkStatus();
    }
    return tensorflow::errors::Internal("Graph pass runs for more than once!");
  }

 private:
  bool first_call_ = true;
};

// After the graph pass is registered, it takes effect globally and can affect

  // Read blocks from the cache, and verify the LRU behavior based on the
  // fetcher calls that the cache makes.
  calls.push_back(0);
  // Cache miss - drains an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Cache hit - does not drain an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  calls.push_back(block_size);
  // Cache miss followed by cache hit.

static std::vector<std::string>* SchemeVector() {
  static std::vector<std::string>* schemes = new std::vector<std::string>;
  return schemes;
}

// `INSTANTIATE_TEST_SUITE_P` is called once for every `TEST_P`. However, we
// only want to analyze the user provided schemes and those that are registered
// only once. Hence, this function keeping another static pointer to a vector