code: def __init__(
      self,
      batch_size,
      num_classes,
      num_channels,
      height,
      width,
      one_hot,
      memory_format=torch.contiguous_format,
  ):
      input_data = (
          torch.randn(batch_size, num_channels, height, width)
          .contiguous(memory_format=memory_format)
          .cuda()
          .normal_(0, 1.0)
      )
      if one_hot:
          input_target = torch.empty(batch_size, num_classes).cuda()
          input_target[:, 0] = 1.0
      else:
          input_target = torch.randint(0, num_classes, (batch_size,))
      input_target = input_target.cuda()
  
      self.input_data = input_data
      self.input_target = input_target
  
imports: import os
  import torch
  import numpy
  from PIL import Image
  from functools import partial
  from image_classification.autoaugment import AutoaugmentImageNetPolicy
  import torchvision.datasets
  import torchvision.transforms
label_desc: Use image node_id 1 to create a new SynteticDataLoader object from inherited base classes: object with example: obj = SynteticDataLoader(batch_size, num_classes, num_channels, height, width, one_hot, memory_format)