class_docstr: Applies a GroupLinear transformation layer, as defined `here <https://arxiv.org/abs/1808.09029>`_,
  `here <https://arxiv.org/abs/1911.12385>`_ and `here <https://arxiv.org/abs/2008.00623>`_
  
  Args:
      in_features (int): number of features in the input tensor
      out_features (int): number of features in the output tensor
      n_groups (int): number of groups
      bias (Optional[bool]): use bias or not
      feature_shuffle (Optional[bool]): Shuffle features between groups
  
  Shape:
      - Input: :math:`(N, *, C_{in})`
      - Output: :math:`(N, *, C_{out})`
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code: def __repr__(self):
      repr_str = "{}(in_features={}, out_features={}, groups={}, bias={}, shuffle={})".format(
          self.__class__.__name__,
          self.in_features,
          self.out_features,
          self.n_groups,
          True if self.bias is not None else False,
          self.feature_shuffle,
      )
      return repr_str
  
imports: import argparse
  from typing import Optional
  import torch
  from torch import Tensor, nn
  from torch.nn import functional
  from cvnets.layers.base_layer import BaseLayer
  from utils import logger
label_desc: Use image node_id 6 for calling the GroupLinear obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str