code: def create_x3d(
      *,
      # EfficientX3d model arguments.
      num_classes: int = 400,
      dropout: float = 0.5,
      expansion: str = "XS",
      head_act: str = "identity",
      enable_head: bool = True,
  ):
      """
      This function builds a X3D network with efficient blocks.
      Args:
          num_classes (int): Number of classes in classification.
          dropout (float): Dropout rate used for training the network.
          expansion (str): Expansion for X3D. Possible options: 'XS', 'S', 'M', 'L'.
          head_act (str): The activation function to be applied in head, should be a key
              in dict supported_act_functions (see activation_functions.py for more info
              about supported activations). Currently ReLU ('relu'), Swish ('swish'),
              Hardswish ('hswish'), Identity ('identity') are supported.
          enable_head (bool): Whether X3D model provides head.
      """
      return EfficientX3d(
          num_classes=num_classes,
          dropout=dropout,
          expansion=expansion,
          head_act=head_act,
          enable_head=enable_head,
      )
  
file_path: pytorchvideo/pytorchvideo/models/accelerator/mobile_cpu/efficient_x3d.py
imports: from collections import OrderedDict
  import torch.nn
  from pytorchvideo.layers.accelerator.mobile_cpu.activation_functions import supported_act_functions
  from pytorchvideo.layers.accelerator.mobile_cpu.convolutions import Conv3d5x1x1BnAct, Conv3dPwBnAct, Conv3dTemporalKernel1BnAct
  from pytorchvideo.layers.accelerator.mobile_cpu.fully_connected import FullyConnected
  from pytorchvideo.layers.accelerator.mobile_cpu.pool import AdaptiveAvgPool3dOutSize1
  from .residual_blocks import X3dBottleneckBlock
label_desc: Use image node_id 1 for calling a global function with example usage: create_x3d() and returns: EfficientX3d