fix type bug

Author: zewenli98

py/torch_tensorrt/dynamo/conversion/impl/normalization/ops.py

@@ -48,16 +48,16 @@ def batch_norm(
         assert input.shape[1] != -1, "Channel dim can't be dynamic for batch norm."
 
     if weight is None:
-        weight = np.array(1.0)
+        weight = 1.0
 
     if bias is None:
-        bias = np.array(0.0)
+        bias = 0.0
 
     if running_mean is None:
-        running_mean = np.array(0.0)
+        running_mean = 0.0
 
     if running_var is None:
-        running_var = np.array(1.0)
+        running_var = 1.0
 
     scale = cast(torch.Tensor, to_numpy(weight)) / np.sqrt(
         cast(torch.Tensor, to_numpy(running_var)) + eps
@@ -115,10 +115,10 @@ def layer_norm(
         )
 
     if weight is None:
-        weight = np.array(1.0)
+        weight = to_numpy(1.0)
 
     if bias is None:
-        bias = np.array(0.0)
+        bias = to_numpy(0.0)
 
     gamma = (
         weight.detach().cpu().float().numpy()
@@ -181,10 +181,10 @@ def layer_norm_no_plugin(
         )
 
     if weight is None:
-        weight = np.array(1.0)
+        weight = to_numpy(1.0)
 
     if bias is None:
-        bias = np.array(0.0)
+        bias = to_numpy(0.0)
 
     shape = weight.shape
     broadcasted_shape = (1,) * (len(input.shape) - len(shape)) + shape