code: def get_media_tensor(self, image_path):
      from PIL import Image
  
      image = Image.open(image_path).convert("RGB")
  
      if self.data_cfg.image_aspect_ratio == "keep":
          max_hw, min_hw = max(image.size), min(image.size)
          aspect_ratio = max_hw / min_hw
          max_len, min_len = 448, 224
          shortest_edge = int(min(max_len / aspect_ratio, min_len))
          image = self.processor.preprocess(
              image,
              return_tensors="pt",
              do_center_crop=False,
              size={"shortest_edge": shortest_edge},
          )["pixel_values"][0]
      elif self.data_cfg.image_aspect_ratio == "pad":
  
          def expand2square(pil_img, background_color):
              width, height = pil_img.size
              if width == height:
                  return pil_img
              elif width > height:
                  result = Image.new(
                      pil_img.mode, (width, width), background_color
                  )
                  result.paste(pil_img, (0, (width - height) // 2))
                  return result
              else:
                  result = Image.new(
                      pil_img.mode, (height, height), background_color
                  )
                  result.paste(pil_img, ((height - width) // 2, 0))
                  return result
  
          image = expand2square(
              image,
              tuple(int(x * 255) for x in self.processor.image_mean),
          )
          image = self.processor.preprocess(image, return_tensors="pt")[
              "pixel_values"
          ][0]
      else:
          image = self.processor.preprocess(image, return_tensors="pt")[
              "pixel_values"
          ][0]
  
      model_cfg = self.model.cfg
  
      if model_cfg.precision in [16, "16", "16-mixed"]:
          media = image.type(torch.float16)
      elif model_cfg.precision in [32, "32", "32-true"]:
          media = image.type(torch.float32)
      else:
          media = image.type(torch.bfloat16)
  
      return media.unsqueeze(dim=0).unsqueeze(dim=0).unsqueeze(dim=0)
  
imports: import abc
  import copy
  import os
  import re
  import warnings
  from typing import List, Set, Tuple
  import torch
  from transformers import CLIPImageProcessor
  from nemo.collections.nlp.modules.common.lm_utils import pad_batch
  from nemo.collections.nlp.modules.common.megatron.utils import get_ltor_masks_and_position_ids
label_desc: Use image node_id 6 for calling the NevaModelTextGenerationStrategy obj's underlying member method code with example usage: obj.get_media_tensor(image_path) and returns: media, pil_img, result, result