class_docstr: Conv3d with BatchNorm & Activation
  
  Args:
      in_planes (`int`):
          Number of input channels.
      out_planes (`int`):
          Number of output channels.
      kernel_size (Union[int, Tuple[int, int]]):
          Kernel size for conv layer.
      padding (`Union[int, Tuple[int, int]]`):
          Padding for conv layer.
      stride (`Union[int, Tuple[int, int]]`):
          Stride for conv layer.
      norm_layer (`nn.Module`):
          Norm layer.
      eps (`float`):
          The number of eps for norm layer.
      activation_layer (`nn.Module`):
          Activation layer
      **kwargs (`Any`):
          Other parameters.
code: def __init__(
      self,
      in_planes: int,
      out_planes: int,
      *,
      kernel_size: Union[int, Tuple[int, int, int]],
      padding: Union[int, Tuple[int, int, int]],
      stride: Union[int, Tuple[int, int, int]] = 1,
      groups: int = 1,
      norm_layer: Optional[Callable[..., nn.Module]] = None,
      eps: float = 0.001,
      activation_layer: Optional[Callable[..., nn.Module]] = None,
      **kwargs: Any,
  ) -> None:
      kernel_size = _triple(kernel_size)
      stride = _triple(stride)
      padding = _triple(padding)
      if norm_layer is None:
          norm_layer = nn.Identity
      if activation_layer is None:
          activation_layer = nn.Identity
      self.kernel_size = kernel_size
      self.stride = stride
      dict_layers = OrderedDict(
          {
              "conv3d": nn.Conv3d(
                  in_planes,
                  out_planes,
                  kernel_size=kernel_size,
                  stride=stride,
                  padding=padding,
                  groups=groups,
                  **kwargs,
              ),
              "norm": norm_layer(out_planes, eps=eps),
              "act": activation_layer(),
          }
      )
  
      self.out_channels = out_planes
      super().__init__(dict_layers)
  
file_path: towhee/towhee/models/layers/conv_bn_activation.py
imports: from collections import OrderedDict
  from typing import Any, Callable, Optional, Tuple, Union
  from torch.nn.modules.utils import _triple, _pair
  from torch import nn
label_desc: Use image node_id 1 to create a new Conv3DBNActivation object from inherited base classes: nn with example: obj = Conv3DBNActivation(in_planes, out_planes)