Create example.py

example.py

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+# example.py
+import torch
+from model import MeteoGAN  # Import your model class
+
+# Load the pre-trained model
+model = MeteoGAN(in_channels=3, out_channels=3, upscale=4)
+model.load_state_dict(torch.load("best_netG.pth"))  # Load model weights
+
+# Create a random input tensor (e.g., for testing purposes)
+input_tensor = torch.rand(1, 3, 128, 128)  # Batch size 1, 3 channels, 128x128 image
+
+# Perform inference
+output_tensor = model(input_tensor)
+
+# Print input and output shapes
+print("Input shape:", input_tensor.shape)
+print("Output shape:", output_tensor.shape)