char buffer[BUFFER_SIZE];
  std::strncpy(buffer, "VALID", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "padding", buffer, std::strlen(buffer));
  // Overwriting value in "buffer", should be fine since TFE_Op
  // shouldn't be holding on to it.
  std::strncpy(buffer, "NHWC", BUFFER_SIZE);
  TFE_OpSetAttrString(op, "data_format", buffer, std::strlen(buffer));

  TFE_OpSetAttrType(op, "T", TF_FLOAT);

      block_size, block_count * block_size, 0, fetcher);
  std::vector<char> out;
  out.resize(block_count, 0);
  // The cache has space for `block_count` blocks. The loop with i = 0 should
  // fill the cache, and the loop with i = 1 should be all cache hits. The
  // fetcher checks that it is called once and only once for each offset (to
  // fetch the corresponding block).
  for (int i = 0; i < 2; i++) {

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1.  The issue should be related to the repo it is created in.

**Here's why we have this policy:** We want to focus on the work that benefits
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#include "tensorflow/core/public/session.h"

namespace tensorflow {
class Device;
class DeviceMgr;
class ServerInterface;
}  // namespace tensorflow

// Internal structures used by the C API. These are likely to change and should
// not be depended on.

struct TF_SessionOptions {
  tensorflow::SessionOptions options;
};

struct TF_DeprecatedSession {
  tensorflow::Session* session;
};

struct TF_Library {

        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) {

  virtual AbstractTensorInterface* Resolve(Status* status) = 0;

  std::string DebugString() const override;

  // Returns a Boolean hint indicating whether callers should prefer
  // `SummarizeValue` to resolving this handle and formatting the tensor.
  //
  // For example some tensor handles may represent distributed values, in which

    ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
    TF_DeleteStatus(status);
  }

  // 6. Read `var` using `ctx_1`. This read should succeed since `ctx_1` was
  // created with `isolate_session_state` set to false, and update should
  // preserve it.
  {
    // Create a handle to `var`, using `ctx_1`.
    TFE_TensorHandle* var_handle =

  EXPECT_TRUE(cache2.Lookup("d", &value));
  EXPECT_EQ(value, 4);
  EXPECT_TRUE(cache2.Lookup("e", &value));
  EXPECT_EQ(value, 5);
}

TEST(ExpiringLRUCacheTest, LookupOrCompute) {
  // max_age of 0 means we should always compute.
  uint64 num_compute_calls = 0;
  tf_gcs_filesystem::ExpiringLRUCache<int>::ComputeFunc compute_func =
      [&num_compute_calls](const string& key, int* value, TF_Status* status) {

}

void NoOpDeallocator(void* data, size_t, void*) {}

TEST(CAPI, MalformedTensor) {
  // See https://github.com/tensorflow/tensorflow/issues/7394
  // num_dims = 0 implies a scalar, so should be backed by at least 4 bytes of
  // data.
  TF_Tensor* t =
      TF_NewTensor(TF_FLOAT, nullptr, 0, nullptr, 0, &NoOpDeallocator, nullptr);
  ASSERT_TRUE(t == nullptr);
}

TEST(CAPI, AllocateTensor) {

  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  TF_AbstractOpSetOpName(placeholder_op, "my_ph", status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // This should fail.
  TF_AbstractOpSetOpType(placeholder_op, "Placeholder", status.get());
  ASSERT_EQ(TF_FAILED_PRECONDITION, TF_GetCode(status.get()));

  TF_DeleteAbstractOp(placeholder_op);
  TF_DeleteExecutionContext(graph_ctx);