class_docstr: X3D model builder. It builds a X3D network backbone, which is a ResNet.
  
  Christoph Feichtenhofer.
  "X3D: Expanding Architectures for Efficient Video Recognition."
  https://arxiv.org/abs/2004.04730
class_docstr_tok: ["X3D","model","builder",".","It","builds","a","X3D","network","backbone",",","which","is","a","ResNet",".","Christoph","Feichtenhofer",".","``","X3D",":","Expanding","Architectures","for","Efficient","Video","Recognition",".","''","https",":","\/\/arxiv.org\/abs\/2004.04730"]
docstr: Round number of layers based on depth multiplier.
code: def _round_repeats(self, repeats, multiplier):
      """Round number of layers based on depth multiplier."""
      multiplier = multiplier
      if not multiplier:
          return repeats
      return int(math.ceil(multiplier * repeats))
  
imports: import math
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  import slowfast.utils.logging
  import slowfast.utils.weight_init_helper
  from slowfast.models.attention import MultiScaleBlock
  from slowfast.models.batchnorm_helper import get_norm
  from slowfast.models.common import TwoStreamFusion
  from slowfast.models.reversible_mvit import ReversibleMViT
  from slowfast.models.utils import calc_mvit_feature_geometry, get_3d_sincos_pos_embed, round_width, validate_checkpoint_wrapper_import
  from .None import head_helper, operators, resnet_helper, stem_helper
  from .build import MODEL_REGISTRY
label_desc: Use image node_id 2 for calling the X3D obj's underlying member method code with example usage: obj._round_repeats(repeats, multiplier) and returns: int, repeats