class_docstr: Configurable block used for Convolution3d-Normalization-Activation blocks.
  
  Args:
      in_channels (int): Number of channels in the input video.
      out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
      kernel_size: (int, optional): Size of the convolving kernel. Default: 3
      stride (int, optional): Stride of the convolution. Default: 1
      padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation``
      groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
      norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm3d``
      activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU``
      dilation (int): Spacing between kernel elements. Default: 1
      inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
      bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: Union[int, Tuple[int, int, int]] = 3,
      stride: Union[int, Tuple[int, int, int]] = 1,
      padding: Optional[Union[int, Tuple[int, int, int], str]] = None,
      groups: int = 1,
      norm_layer: Optional[
          Callable[..., torch.nn.Module]
      ] = torch.nn.BatchNorm3d,
      activation_layer: Optional[
          Callable[..., torch.nn.Module]
      ] = torch.nn.ReLU,
      dilation: Union[int, Tuple[int, int, int]] = 1,
      inplace: Optional[bool] = True,
      bias: Optional[bool] = None,
  ) -> None:
      super().__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          padding,
          groups,
          norm_layer,
          activation_layer,
          dilation,
          inplace,
          bias,
          torch.nn.Conv3d,
      )
  
imports: import warnings
  from typing import Callable, List, Optional, Sequence, Tuple, Union
  import torch
  from torch import Tensor
  from ..utils import _log_api_usage_once, _make_ntuple
label_desc: Use image node_id 1 to create a new Conv3dNormActivation object from inherited base classes: ConvNormActivation with example: obj = Conv3dNormActivation(in_channels, out_channels, kernel_size, stride, padding, groups, norm_layer, activation_layer, dilation, inplace, bias)