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logging.info(f\"Header Info: {header}\")\r\n\r\n        pt = Imath.PixelType(Imath.PixelType.HALF)\r\n        images = {}\r\n\r\n        for channel_name, channel_info in channels.items():\r\n            x_sampling, y_sampling = channel_info.xSampling, channel_info.ySampling\r\n            expected_width = size[0] // x_sampling\r\n            expected_height = size[1] // y_sampling\r\n            expected_size = expected_width * expected_height\r\n            logging.info(f\"Channel '{channel_name}' subsampling: {x_sampling}x{y_sampling}, Expected size: {expected_size}\")\r\n\r\n            # Load the channel data to compute actual size\r\n            buffer = exr_file.channel(channel_name, pt)\r\n            data = np.frombuffer(buffer, dtype=np.float16)\r\n            actual_size = data.size\r\n\r\n            logging.info(f\"Channel '{channel_name}' actual size: {actual_size}\")\r\n\r\n            if actual_size != expected_size:\r\n                logging.error(f\"Channel 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Expected: {expected_size}, Actual: {actual_size}\")\r\n            else:\r\n                logging.info(f\"Channel '{channel_name}' size matches expected value.\")\r\n                reshaped_data = data.reshape((expected_height, expected_width))\r\n                images[channel_name] = reshaped_data\r\n\r\n        def render_image(images, channels, full_res_size):\r\n            plt.figure(figsize=(10, 10))\r\n\r\n            # Check if we can render the image based on available channels\r\n            if len(images) >= 3:\r\n                # Assuming channels are in BY, RY, Y order; adjust as necessary\r\n                combined_image = np.zeros((full_res_size[1], full_res_size[0], 3))\r\n                channel_order = ['Y', 'RY', 'BY']  # Adjust if needed\r\n\r\n                for i, channel_name in enumerate(channel_order):\r\n                    if channel_name in channels:\r\n                        logging.info(f\"Using channel '{channel_name}' for combined image.\")\r\n                        if images[channel_name].shape != (full_res_size[1], full_res_size[0]):\r\n                            logging.info(f\"Resizing channel '{channel_name}' from {images[channel_name].shape} to {full_res_size[1], full_res_size[0]}\")\r\n                            resized_channel = resize(images[channel_name], (full_res_size[1], full_res_size[0]), anti_aliasing=True)\r\n                        else:\r\n                            resized_channel = images[channel_name]\r\n                        combined_image[..., i] = resized_channel\r\n\r\n                combined_image = np.clip(combined_image, 0, 1)\r\n                plt.imshow(combined_image)\r\n            elif 'Y' in channels and len(images) == 1:\r\n                # 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