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:
      in_features: first dimension of matrix A.
      out_features: second dimension of matrix A.
      bias: If true, add bias
      gather_output: If true, call all-gather on output and make Y avaiable
                     to all GPUs, otherwise, every GPU will have its output
                     which is Y_i = XA_i
      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.
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code: def get_master_weight(self) -> torch.Tensor:
      return gather_from_model_parallel_region(
          self.weight.data.transpose(0, 1)
      ).transpose_(0, 1)
  
imports: from typing import Callable, Optional
  import torch
  import torch.nn.functional
  import torch.nn.init
  from torch.nn.parameter import Parameter
  from .initialize import get_model_parallel_rank, get_model_parallel_world_size
  from .mappings import copy_to_model_parallel_region, gather_from_model_parallel_region, reduce_from_model_parallel_region, scatter_to_model_parallel_region
  from .utils import VocabUtility, divide_and_check_no_remainder
label_desc: Use image node_id 2 for calling the ColumnParallelLinear obj's underlying member method code with example usage: obj.get_master_weight() and returns: gather_from_model_parallel_region