text: import torch
  import math
  from .batch_ops import batch_matmul, batch_bias_add
  from .masked_module import MaskedModule
  
  
  class Linear(MaskedModule):
      __constants__ = ['in_features', 'out_features']
  
      def __init__(self, in_features, out_features, bias=True):
          super(Linear, self).__init__()
          self.in_features = in_features
          self.out_features = out_features
          self.weight = torch.nn.Parameter(torch.Tensor(out_features, in_features))
          self.bias = torch.nn.Parameter(torch.Tensor(out_features)) if bias else None
          self.reset_parameters()
  
      def reset_parameters(self):
          torch.nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
          if self.bias is not None:
              fan_in, _ = torch.nn.init._calculate_fan_in_and_fan_out(self.weight)
              bound = 1 / math.sqrt(fan_in)
              torch.nn.init.uniform_(self.bias, -bound, bound)
  
      def forward(self, input):
          front_shape = list(input.shape[:-1])
          input = input.flatten(end_dim=-2)
  
          res = batch_matmul(input, self.weight.transpose(-1,-2))
          if self.bias is not None:
              res = batch_bias_add(res, self.bias)
  
          return res.view(*front_shape, res.shape[-1])
  
      def extra_repr(self):
          return 'in_features={}, out_features={}, bias={}'.format(
              self.in_features, self.out_features, self.bias is not None
          )