diff --git a/torch/utils/tensorboard/writer.py b/torch/utils/tensorboard/writer.py
index cd7f751a32f5f7..5bca7cf91ea9a5 100644
--- a/torch/utils/tensorboard/writer.py
+++ b/torch/utils/tensorboard/writer.py
@@ -297,21 +297,25 @@ def add_hparams(
            :scale: 50 %
 
         """
-        torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
-        if type(hparam_dict) is not dict or type(metric_dict) is not dict:
-            raise TypeError('hparam_dict and metric_dict should be dictionary.')
-        exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
-
-        if not run_name:
-            run_name = str(time.time())
-        logdir = os.path.join(self._get_file_writer().get_logdir(), run_name)
-        with SummaryWriter(log_dir=logdir) as w_hp:
-            w_hp.file_writer.add_summary(exp)
-            w_hp.file_writer.add_summary(ssi)
-            w_hp.file_writer.add_summary(sei)
-            for k, v in metric_dict.items():
-                w_hp.add_scalar(k, v)
-
+        def add_hparams(self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None):
+  torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
+  exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
+  self.file_writer.add_summary(exp)
+  self.file_writer.add_summary(ssi)
+  self.file_writer.add_summary(sei)
+  for k, v in metric_dict.items():
+    if v is not None:
+      self.add_scalar(k, v)
+    from torch.utils.tensorboard import SummaryWriter
+for i in range(5):
+  save_metrics = {'train/acc': None, 'train/loss': None}
+  writer = SummaryWriter(f'runs/{i}')
+  for step in range(50):
+    writer.add_scalar('train/acc', 10*i+step, step)
+    writer.add_scalar('train/loss', 10*i - step, step)
+    writer.add_hparams({'lr': 0.1*i, 'bsize': i}, save_metrics)
+    writer.close()
+    
     def add_scalar(
         self,
         tag,