subsequently concatenated.
    * At groups= "in_channels", each input channel is convolved
      with its own set of filters (of size
      \frac{\text{out\_channels}}{\text{in\_channels}}).

The parameters "kernel_size", "stride", "padding", "dilation" can
   either be:
  * a single "int" -- in which case the same value is used for the
    height and width dimension

  * a "tuple" of two ints -- in which case, the first *int* is
    used for the height dimension, and the second *int* for the
    width dimension

Note:
 When *groups == in_channels* and *out_channels == K *
 in_channels*, where *K* is a positive integer, this operation is
 also known as a "depthwise convolution".In other words, for an
 input of size (N, C_{in}, L_{in}), a depthwise convolution with a
 depthwise multiplier *K* can be performed with the arguments
 (C_\text{in}=C_\text{in}, C_\text{out}=C_\text{in} \times
 \text{K}, ..., \text{groups}=C_\text{in}).
