11"""Psuedo-irradience forecastor/labeller""" 
2+ 
23import  einops 
34import  torch 
45import  torch .nn  as  nn 
@@ -68,7 +69,7 @@ def __init__(
6869                in_channels = input_channels ,
6970                out_channels = conv3d_channels ,
7071                kernel_size = (kernel_size , kernel_size , kernel_size ),
71-                 padding = (1 ,0 , 0 ),
72+                 padding = (1 ,  0 ,  0 ),
7273            )
7374        )
7475        for  i  in  range (0 , num_layers ):
@@ -77,7 +78,7 @@ def __init__(
7778                    in_channels = conv3d_channels ,
7879                    out_channels = conv3d_channels ,
7980                    kernel_size = (kernel_size , kernel_size , kernel_size ),
80-                     padding = (1 ,0 , 0 ),
81+                     padding = (1 ,  0 ,  0 ),
8182                )
8283            )
8384
@@ -95,9 +96,7 @@ def __init__(
9596        # Small head model to convert from latent space to PV generation for training 
9697        # Input is per-pixel input data, this will be 
9798        # reshaped to the same output steps as the latent head 
98-         self .pv_meta_input  =  nn .Linear (
99-             pv_meta_input_channels , out_features = hidden_dim 
100-         )
99+         self .pv_meta_input  =  nn .Linear (pv_meta_input_channels , out_features = hidden_dim )
101100
102101        # Output is forecast steps channels, each channel is a timestep 
103102        # For labelling, this should be 1, forecasting the middle 
@@ -142,7 +141,5 @@ def forward(self, x: torch.Tensor, pv_meta: torch.Tensor = None, output_latents:
142141        x  =  torch .cat ([x , pv_meta ], dim = 1 )
143142        # Get pv_meta_output 
144143        x  =  self .pv_meta_output (x )
145-         x  =  F .relu (
146-             self .pv_meta_output2 (x )
147-         )  # Generation can only be positive or 0, so ReLU 
144+         x  =  F .relu (self .pv_meta_output2 (x ))  # Generation can only be positive or 0, so ReLU 
148145        return  x 
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