with its own set of filters (of size
          \frac{\text{out_channels}}{\text{in_channels}}).
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}).

Note:
 In some circumstances when given tensors on a CUDA device and
 using CuDNN, this operator may select a nondeterministic
 algorithm to increase performance. If this is undesirable, you
 can try to make the operation deterministic (potentially at a
 performance cost) by setting "torch.backends.cudnn.deterministic
 = True". See Reproducibility for more information.

Note: