class_docstr: As [`equinox.nn.Conv`][] with `num_spatial_dims=3`.
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: Union[int, Sequence[int]],
      stride: Union[int, Sequence[int]] = (1, 1, 1),
      padding: Union[int, Sequence[int], Sequence[tuple[int, int]]] = (
          0,
          0,
          0,
      ),
      dilation: Union[int, Sequence[int]] = (1, 1, 1),
      groups: int = 1,
      use_bias: bool = True,
      *,
      key: PRNGKeyArray,
  ):
      super().__init__(
          num_spatial_dims=3,
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
          use_bias=use_bias,
          key=key,
      )
  
imports: import itertools
  import math
  from collections.abc import Callable, Sequence
  from typing import Optional, TypeVar, Union
  import jax
  import jax.lax
  import jax.numpy
  import jax.random
  import numpy
  from jaxtyping import Array, PRNGKeyArray
  from .._module import field, Module
  from ._misc import all_sequences
label_desc: Use image node_id 1 to create a new Conv3d object from inherited base classes: Conv with example: obj = Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, use_bias)