status->status = s->session->PRunSetup(input_names, output_names,
                                         target_oper_names, &new_handle);
  if (status->status.ok()) {
    char* buf = new char[new_handle.size() + 1];
    memcpy(buf, new_handle.c_str(), new_handle.size() + 1);
    *handle = buf;
  }
}

void TF_PRun(TF_DeprecatedSession* s, const char* handle,
             // Input tensors

  ops->read_only_memory_region_ops = static_cast<TF_ReadOnlyMemoryRegionOps*>(
      plugin_memory_allocate(TF_READ_ONLY_MEMORY_REGION_OPS_SIZE));
  ops->read_only_memory_region_ops->cleanup =
      tf_read_only_memory_region::Cleanup;
  ops->read_only_memory_region_ops->data = tf_read_only_memory_region::Data;
  ops->read_only_memory_region_ops->length = tf_read_only_memory_region::Length;

                        char* buffer, TF_Status* status) -> int64_t {
    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_Tensor* FloatTensor2x2(const float* values) {
    const int64_t dims[2] = {2, 2};
    TF_Tensor* t = TF_AllocateTensor(TF_FLOAT, dims, 2, sizeof(float) * 4);
    memcpy(TF_TensorData(t), values, sizeof(float) * 4);
    return t;
  }

  TF_Operation* FloatConst2x2(TF_Graph* graph, TF_Status* s,
                              const float* values, const char* name) {

var (
	memTotalMD     = NewGaugeMD(memTotal, "Total memory on the node")
	memUsedMD      = NewGaugeMD(memUsed, "Used memory on the node")
	memUsedPercMD  = NewGaugeMD(memUsedPerc, "Used memory percentage on the node")
	memFreeMD      = NewGaugeMD(memFree, "Free memory on the node")
	memBuffersMD   = NewGaugeMD(memBuffers, "Buffers memory on the node")
	memCacheMD     = NewGaugeMD(memCache, "Cache memory on the node")

  ops->read_only_memory_region_ops = static_cast<TF_ReadOnlyMemoryRegionOps*>(
      plugin_memory_allocate(TF_READ_ONLY_MEMORY_REGION_OPS_SIZE));
  ops->read_only_memory_region_ops->cleanup =
      tf_read_only_memory_region::Cleanup;
  ops->read_only_memory_region_ops->data = tf_read_only_memory_region::Data;
  ops->read_only_memory_region_ops->length = tf_read_only_memory_region::Length;

    if (file != null) {
      return new FileInputStream(file);
    } else {
      // requireNonNull is safe because we always have either `file` or `memory`.
      requireNonNull(memory);
      return new ByteArrayInputStream(memory.getBuffer(), 0, memory.getCount());
    }
  }

  /**
   * Calls {@link #close} if not already closed, and then resets this object back to its initial

  ops->scheme = strdup(uri);
}

void TF_InitPlugin(TF_FilesystemPluginInfo* info) {
  info->plugin_memory_allocate = plugin_memory_allocate;
  info->plugin_memory_free = plugin_memory_free;
  info->num_schemes = 2;
  info->ops = static_cast<TF_FilesystemPluginOps*>(
      plugin_memory_allocate(info->num_schemes * sizeof(info->ops[0])));
  ProvideFilesystemSupportFor(&info->ops[0], "");

<p>
Go approaches its memory model in much the same way as the rest of the language,
aiming to keep the semantics simple, understandable, and useful.
This section gives a general overview of the approach and should suffice for most programmers.
The memory model is specified more formally in the next section.
</p>

<p>
A data race is defined as

| `minio_system_memory_total`      | `gauge` | Total memory on the node           | `server` |
| `minio_system_memory_buffers`    | `gauge` | Buffers memory on the node         | `server` |
| `minio_system_memory_cache`      | `gauge` | Cache memory on the node           | `server` |
| `minio_system_memory_shared`     | `gauge` | Shared memory on the node          | `server` |