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Fix Codacy Warnings (#477)

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Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
Filippo Olivo
2025-03-10 15:38:45 +01:00
committed by Nicola Demo
parent e3790e049a
commit 4177bfbb50
157 changed files with 3473 additions and 3839 deletions
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25
pina/model/block/residual.py
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@@ -1,5 +1,9 @@
"""
TODO: Add title.
"""
import torch
import torch.nn as nn
from torch import nn
from ...utils import check_consistency
@@ -35,7 +39,8 @@ class ResidualBlock(nn.Module):
(first block).
:param bool spectral_norm: Apply spectral normalization to feedforward
layers, defaults to False.
:param torch.nn.Module activation: Cctivation function after first block.
:param torch.nn.Module activation: Cctivation function after first
block.
"""
super().__init__()
@@ -81,19 +86,17 @@ class ResidualBlock(nn.Module):
return nn.utils.spectral_norm(x) if self._spectral_norm else x
import torch
import torch.nn as nn
class EnhancedLinear(torch.nn.Module):
"""
A wrapper class for enhancing a linear layer with activation and/or dropout.
:param layer: The linear layer to be enhanced.
:type layer: torch.nn.Module
:param activation: The activation function to be applied after the linear layer.
:param activation: The activation function to be applied after the linear
layer.
:type activation: torch.nn.Module
:param dropout: The dropout probability to be applied after the activation (if provided).
:param dropout: The dropout probability to be applied after the activation
(if provided).
:type dropout: float
:Example:
@@ -110,9 +113,11 @@ class EnhancedLinear(torch.nn.Module):
:param layer: The linear layer to be enhanced.
:type layer: torch.nn.Module
:param activation: The activation function to be applied after the linear layer.
:param activation: The activation function to be applied after the
linear layer.
:type activation: torch.nn.Module
:param dropout: The dropout probability to be applied after the activation (if provided).
:param dropout: The dropout probability to be applied after the
activation (if provided).
:type dropout: float
"""
super().__init__()