code: def _get_random_batch_indices(
      self,
      seq_dataset,
      n_batches=2,
      min_batch_size=5,
      max_batch_size=10,
  ):
      batch_indices = []
      for _ in range(n_batches):
          batch_size = torch.randint(
              low=min_batch_size, high=max_batch_size, size=(1,)
          )
          batch_indices.append(
              torch.randperm(len(seq_dataset))[:batch_size]
          )
  
      return batch_indices
  
imports: import contextlib
  import dataclasses
  import itertools
  import math
  import os
  import unittest
  import lpips
  import numpy
  import torch
  from pytorch3d.implicitron.dataset.frame_data import FrameData
  from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
  from pytorch3d.implicitron.evaluation.evaluate_new_view_synthesis import eval_batch
  from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
  from pytorch3d.implicitron.models.generic_model import GenericModel
  from pytorch3d.implicitron.models.model_dbir import ModelDBIR
  from pytorch3d.implicitron.tools.config import expand_args_fields, registry
  from pytorch3d.implicitron.tools.metric_utils import calc_psnr, eval_depth
  from pytorch3d.implicitron.tools.utils import dataclass_to_cuda_
  from .common_resources import get_skateboard_data, provide_lpips_vgg
label_desc: Use image node_id 6 for calling the TestEvaluation obj's underlying member method code with example usage: obj._get_random_batch_indices(seq_dataset, n_batches, min_batch_size, max_batch_size) and returns: batch_indices