def create_conv_patch_embed(
    *,
    in_channels: int,
    out_channels: int,
    conv_kernel_size: Tuple[int] = (1, 16, 16),
    conv_stride: Tuple[int] = (1, 4, 4),
    conv_padding: Tuple[int] = (1, 7, 7),
    conv_bias: bool = True,
    conv: Callable = Conv3d,
) -> Module:
    """
    Creates the transformer basic patch embedding. It performs Convolution, flatten and
    transpose.

    ::

                                        Conv3d
                                           ↓
                                        flatten
                                           ↓
                                       transpose

    Args:
        in_channels (int): input channel size of the convolution.
        out_channels (int): output channel size of the convolution.
        conv_kernel_size (tuple): convolutional kernel size(s).
        conv_stride (tuple): convolutional stride size(s).
        conv_padding (tuple): convolutional padding size(s).
        conv_bias (bool): convolutional bias. If true, adds a learnable bias to the
            output.
        conv (callable): Callable used to build the convolution layer.

    Returns:
        (nn.Module): transformer patch embedding layer.
    """
    conv_module = conv(
        in_channels=in_channels,
        out_channels=out_channels,
        kernel_size=conv_kernel_size,
        stride=conv_stride,
        padding=conv_padding,
        bias=conv_bias,
    )
    return PatchEmbed(patch_model=conv_module)
Creates the transformer basic patch embedding. It performs Convolution, flatten and
transpose.

::

Conv3d
↓
flatten
↓
transpose

Args:
in_channels (int): input channel size of the convolution.
out_channels (int): output channel size of the convolution.
conv_kernel_size (tuple): convolutional kernel size(s).
conv_stride (tuple): convolutional stride size(s).
conv_padding (tuple): convolutional padding size(s).
conv_bias (bool): convolutional bias. If true, adds a learnable bias to the
output.
conv (callable): Callable used to build the convolution layer.

Returns:
(nn.Module): transformer patch embedding layer.
python/aitemplate/frontend/nn/patch_embed.py