// 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);

  void* data = dl_tensor->data;

  if (dl_tensor->byte_offset != 0) {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported byte_offset (", dl_tensor->byte_offset,
        ") from DLPack, must be zero");
    return nullptr;
  }

  size_t total_bytes = dl_tensor->dtype.bits / 8;
  for (int i = 0; i < num_dims; i++) {
    total_bytes *= dims[i];
  }

  if (dl_tensor->strides != nullptr &&

      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
    } else {
      TF_SetStatusFromIOError(status, errno, posix_file->filename);
      break;
    }
  }

}

TF_Tensor* Int32Tensor(int32_t v) {
  const int num_bytes = sizeof(int32_t);
  int32_t* values = new int32_t[1];
  values[0] = v;
  return TF_NewTensor(TF_INT32, nullptr, 0, values, num_bytes,
                      &Int32Deallocator, nullptr);
}

TF_Tensor* DoubleTensor(double v) {
  const int num_bytes = sizeof(double);
  double* values = new double[1];
  values[0] = v;

                                        TF_Status* status) {
  const int num_bytes = v.size() * sizeof(float);
  float* values = new float[v.size()];
  memcpy(values, v.data(), num_bytes);
  int64_t dims = v.size();
  std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
      TF_NewTensor(TF_FLOAT, &dims, 1 /* num_dims */, values, num_bytes,
                   &FloatDeallocator, nullptr),
      TF_DeleteTensor);

    if (file_size != content.size())
      TF_SetStatus(
          status_, TF_DATA_LOSS,
          std::string("expected " + std::to_string(file_size) + " got " +
                      std::to_string(content.size()) + " bytes")
              .c_str());
    return content;
  }

 protected:
  TF_Filesystem* filesystem_;
  TF_Status* status_;

 private:
  std::string root_dir_;
};

    int64_t bytes_transferred;
    if (offset < buf.size()) {
      size_t bytes_to_copy = std::min<size_t>(buf.size() - offset, n);
      memcpy(buffer, buf.data() + offset, bytes_to_copy);
      bytes_transferred = bytes_to_copy;
    } else {
      bytes_transferred = 0;
    }
    TF_SetStatus(status, TF_OK, "");
    return bytes_transferred;
  };

  TF_DeleteBuffer(attr_buf);

  // Set attr
  tensorflow::AttrValue attr;
  attr.set_s("test_attr_value");
  string bytes;
  attr.SerializeToString(&bytes);
  TF_FunctionSetAttrValueProto(func_, "test_attr_name", bytes.data(),
                               bytes.size(), s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);

  // Get attr
  AttrValue read_attr;

#include "tensorflow/c/experimental/filesystem/plugins/posix/copy_file.h"

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses `sendfile` specific to linux after 2.6.33.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;
  // When `sendfile` returns 0 we stop copying and let callers handle this.

    block_size = value * 1024 * 1024;
  }
  const char* max_bytes_env = std::getenv(kMaxCacheSize);
  if (max_bytes_env && absl::SimpleAtoi(max_bytes_env, &value)) {
    max_bytes = static_cast<size_t>(value * 1024 * 1024);
  }
  const char* max_staleness_env = std::getenv(kMaxStaleness);
  if (max_staleness_env && absl::SimpleAtoi(max_staleness_env, &value)) {
    max_staleness = value;
  }