class_docstr: Flip the image horizontally with probability flip_prob.
  Operates on raw arrays (not tensors).
  
  Parameters
  ----------
  flip_prob : float
      The probability with which to flip each image in the batch
      horizontally.
code: def generate_code(self) -> Callable:
      my_range = Compiler.get_iterator()
      flip_prob = self.flip_prob
  
      def flip(images, dst):
          should_flip = rand(images.shape[0]) < flip_prob
          for i in my_range(images.shape[0]):
              if should_flip[i]:
                  dst[i] = images[i, :, ::-1]
              else:
                  dst[i] = images[i]
  
          return dst
  
      flip.is_parallel = True
      return flip
  
file_path: ffcv/ffcv/transforms/flip.py
imports: from dataclasses import replace
  from numpy.random import rand
  from typing import Callable, Optional, Tuple
  from ..pipeline.allocation_query import AllocationQuery
  from ..pipeline.operation import Operation
  from ..pipeline.state import State
  from ..pipeline.compiler import Compiler
label_desc: Use image node_id 2 for calling the RandomHorizontalFlip obj's underlying member method code with example usage: obj.generate_code() and returns: flip, dst