def filter3d(input: Tensor, kernel: Tensor, border_type: str = "replicate", normalized: bool = False) -> Tensor:
    r"""Convolve a tensor with a 3d kernel.

    The function applies a given kernel to a tensor. The kernel is applied
    independently at each depth channel of the tensor. Before applying the
    kernel, the function applies padding according to the specified mode so
    that the output remains in the same shape.

    Args:
        input: the input tensor with shape of
          :math:`(B, C, D, H, W)`.
        kernel: the kernel to be convolved with the input
          tensor. The kernel shape must be :math:`(1, kD, kH, kW)`  or :math:`(B, kD, kH, kW)`.
        border_type: the padding mode to be applied before convolving.
          The expected modes are: ``'constant'``,
          ``'replicate'`` or ``'circular'``.
        normalized: If True, kernel will be L1 normalized.

    Return:
        the convolved tensor of same size and numbers of channels
        as the input with shape :math:`(B, C, D, H, W)`.

    Example:
        >>> input = torch.tensor([[[
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]],
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 5., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]],
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]]
        ... ]]])
        >>> kernel = torch.ones(1, 3, 3, 3)
        >>> filter3d(input, kernel)
        tensor([[[[[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]],
        <BLANKLINE>
                  [[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]],
        <BLANKLINE>
                  [[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]]]]])

    """
    KORNIA_CHECK_IS_TENSOR(input)
    KORNIA_CHECK_SHAPE(input, ["B", "C", "D", "H", "W"])
    KORNIA_CHECK_IS_TENSOR(kernel)
    KORNIA_CHECK_SHAPE(kernel, ["B", "D", "H", "W"])

    KORNIA_CHECK(
        str(border_type).lower() in _VALID_BORDERS,
        f"Invalid border, gotcha {border_type}. Expected one of {_VALID_BORDERS}",
    )

    # prepare kernel
    b, c, d, h, w = input.shape
    tmp_kernel = kernel[:, None, ...].to(device=input.device, dtype=input.dtype)

    if normalized:
        bk, dk, hk, wk = kernel.shape
        tmp_kernel = normalize_kernel2d(tmp_kernel.view(bk, dk, hk * wk)).view_as(tmp_kernel)

    tmp_kernel = tmp_kernel.expand(-1, c, -1, -1, -1)

    # pad the input tensor
    depth, height, width = tmp_kernel.shape[-3:]
    padding_shape: list[int] = _compute_padding([depth, height, width])
    input_pad = pad(input, padding_shape, mode=border_type)

    # kernel and input tensor reshape to align element-wise or batch-wise params
    tmp_kernel = tmp_kernel.reshape(-1, 1, depth, height, width)
    input_pad = input_pad.view(-1, tmp_kernel.size(0), input_pad.size(-3), input_pad.size(-2), input_pad.size(-1))

    # convolve the tensor with the kernel.
    output = F.conv3d(input_pad, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1)

    return output.view(b, c, d, h, w)
Convolve a tensor with a 3d kernel.

    The function applies a given kernel to a tensor. The kernel is applied
    independently at each depth channel of the tensor. Before applying the
    kernel, the function applies padding according to the specified mode so
    that the output remains in the same shape.

    Args:
        input: the input tensor with shape of
          :math:`(B, C, D, H, W)`.
        kernel: the kernel to be convolved with the input
          tensor. The kernel shape must be :math:`(1, kD, kH, kW)`  or :math:`(B, kD, kH, kW)`.
        border_type: the padding mode to be applied before convolving.
          The expected modes are: ``'constant'``,
          ``'replicate'`` or ``'circular'``.
        normalized: If True, kernel will be L1 normalized.

    Return:
        the convolved tensor of same size and numbers of channels
        as the input with shape :math:`(B, C, D, H, W)`.

    Example:
        >>> input = torch.tensor([[[
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]],
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 5., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]],
        ...    [[0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.],
        ...     [0., 0., 0., 0., 0.]]
        ... ]]])
        >>> kernel = torch.ones(1, 3, 3, 3)
        >>> filter3d(input, kernel)
        tensor([[[[[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]],
        <BLANKLINE>
                  [[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]],
        <BLANKLINE>
                  [[0., 0., 0., 0., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 5., 5., 5., 0.],
                   [0., 0., 0., 0., 0.]]]]])

    
kornia/filters/filter.py