class_docstr: Linear layer with column parallelism.
  The linear layer is defined as Y = XA + b. A is parallelized along
  its second dimension as A = [A_1, ..., A_p].
  Arguments:
      input_size: first dimension of matrix A.
      output_size: second dimension of matrix A.
      bias: If true, add bias
      init_method: method to initialize weights. Note that bias is always set
                   to zero.
      stride: For the strided linear layers.
      keep_master_weight_for_test: This was added for testing and should be
                                   set to False. It returns the master weights
                                   used for initialization.
      skip_bias_add: This was added to enable performance optimations where bias
                     can be fused with other elementwise operations. we skip
                     adding bias but instead return it.
code: def __repr__(self):
      return (
          f"Linear(in_features={self.input_size}, out_features={self.output_size}, "
          + f"bias={self.bias is not None}, skip_bias_add={self.skip_bias_add})"
      )
  
file_path: colossalai/examples/tutorial/sequence_parallel/model/layers/linear.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch.nn.init
  from torch.nn import Parameter
label_desc: Use image node_id 3 for calling the Linear obj's underlying member method code with example usage: obj.__repr__() and returns: unknown