Fix AttributeError in _init_weights for LayerNorm

When loading this model for a downstream task like token classification using AutoModelForTokenClassification.from_pretrained, the internal call to init_weights() fails with an AttributeError.
This happens because some LayerNorm layers in the model are defined with elementwise_affine=False, meaning their .weight and .bias attributes are None. The _init_weights method does not check for this before trying to access .data, causing a crash.
This PR adds a check to ensure .weight and .bias are not None before they are accessed, fixing the loading issue and allowing the model to be easily fine-tuned for downstream tasks.

modeling_ltgbert.py

@@ -255,8 +255,10 @@ class LtgbertPreTrainedModel(PreTrainedModel):
         elif isinstance(module, nn.Embedding):
             nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std)
         elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
+            if module.bias is not None:
+                module.bias.data.zero_()
+            if module.weight is not None:
+                module.weight.data.fill_(1.0)
 
 
 class LtgbertModel(LtgbertPreTrainedModel):