code: def preprocess_for_train(
      image_bytes,
      use_bfloat16,
      image_size=IMAGE_SIZE,
      interpolation="bicubic",
  ):
      """Preprocesses the given image for evaluation.
  
      Args:
        image_bytes: `Tensor` representing an image binary of arbitrary size.
        use_bfloat16: `bool` for whether to use bfloat16.
        image_size: image size.
        interpolation: image interpolation method
  
      Returns:
        A preprocessed image `Tensor`.
      """
      resize_method = (
          tf.image.ResizeMethod.BICUBIC
          if interpolation == "bicubic"
          else tf.image.ResizeMethod.BILINEAR
      )
      image = _decode_and_random_crop(
          image_bytes, image_size, resize_method
      )
      image = _flip(image)
      image = tf.reshape(image, [image_size, image_size, 3])
      image = tf.image.convert_image_dtype(
          image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32
      )
      return image
  
file_path: pytorch-image-models/timm/data/tf_preprocessing.py
imports: import tensorflow.compat.v1
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: preprocess_for_train(image_bytes, use_bfloat16, image_size, interpolation) and returns: image