diff --git a/pypots/nn/modules/raindrop/backbone.py b/pypots/nn/modules/raindrop/backbone.py
index 50af4fe0..3d23146b 100644
--- a/pypots/nn/modules/raindrop/backbone.py
+++ b/pypots/nn/modules/raindrop/backbone.py
@@ -13,27 +13,6 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import TransformerEncoderLayer, TransformerEncoder
-from torch.nn.parameter import Parameter
-
-from ....utils.logging import logger
-
-try:
-    from .layers import PositionalEncoding, ObservationPropagation
-    from torch_geometric.nn.inits import glorot
-except ImportError as e:
-    logger.error(
-        f"❌ {e}\n"
-        "Note torch_geometric is missing, please install it with "
-        "'pip install torch_geometric torch_scatter torch_sparse' or "
-        "'conda install -c pyg pyg pytorch-scatter pytorch-sparse'"
-    )
-except NameError as e:
-    logger.error(
-        f"❌ {e}\n"
-        "Note torch_geometric is missing, please install it with "
-        "'pip install torch_geometric torch_scatter torch_sparse' or "
-        "'conda install -c pyg pyg pytorch-scatter pytorch-sparse'"
-    )
 
 
 class BackboneRaindrop(nn.Module):
@@ -53,7 +32,18 @@ def __init__(
         sensor_wise_mask=False,
         static=False,
     ):
+
+        try:
+            from .layers import PositionalEncoding, ObservationPropagation
+        except (ImportError, NameError) as e:
+            raise ImportError(
+                f"❌ {e}. Note that torch_geometric is missing, please install it with "
+                "'pip install torch_geometric torch_scatter torch_sparse' or "
+                "'conda install -c pyg pyg pytorch-scatter pytorch-sparse'"
+            )
+
         super().__init__()
+
         self.n_layers = n_layers
         self.n_features = n_features
         self.d_model = d_model
@@ -91,7 +81,7 @@ def __init__(
             )
         self.transformer_encoder = TransformerEncoder(encoder_layers, n_layers)
 
-        self.R_u = Parameter(torch.Tensor(1, self.n_features * self.d_ob))
+        self.R_u = nn.Parameter(torch.Tensor(1, self.n_features * self.d_ob))
 
         self.ob_propagation = ObservationPropagation(
             in_channels=max_len * self.d_ob,
@@ -126,7 +116,7 @@ def init_weights(self):
         self.encoder.weight.data.uniform_(-init_range, init_range)
         if self.static:
             self.static_emb.weight.data.uniform_(-init_range, init_range)
-        glorot(self.R_u)
+        nn.init.xavier_uniform(self.R_u)  # xavier_uniform also known as glorot
 
     def forward(
         self,
@@ -134,7 +124,7 @@ def forward(
         timestamps: torch.Tensor,
         lengths: torch.Tensor,
     ) -> Tuple[torch.Tensor, ...]:
-        """Forward processing of BRITS.
+        """Forward processing of Raindrop.
 
         Parameters
         ----------
diff --git a/pypots/nn/modules/raindrop/layers.py b/pypots/nn/modules/raindrop/layers.py
index d70fba66..0c56a5ba 100644
--- a/pypots/nn/modules/raindrop/layers.py
+++ b/pypots/nn/modules/raindrop/layers.py
@@ -18,8 +18,6 @@
 from torch.nn import init
 from torch.nn.parameter import Parameter
 
-from ....utils.logging import logger
-
 try:
     from torch_geometric.nn.conv import MessagePassing
     from torch_geometric.nn.inits import glorot
@@ -27,13 +25,10 @@
     from torch_geometric.utils import softmax
     from torch_scatter import scatter
     from torch_sparse import SparseTensor
-except ImportError as e:
-    logger.error(
-        f"❌ {e}\n"
-        "Note torch_geometric is missing, please install it with "
-        "'pip install torch_geometric torch_scatter torch_sparse' or "
-        "'conda install -c pyg pyg pytorch-scatter pytorch-sparse'"
-    )
+except ImportError:
+    # Modules here only for Raindrop model, and torch_geometric import errors are caught in BackboneRaindrop.
+    # Hence, we can pass them here.
+    pass
 
 
 class PositionalEncoding(nn.Module):