


    m.weight.fill_(1.0)
     >>>         print(m.weight)
     >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
     >>> net.apply(init_weights)
     Linear(in_features=2, out_features=2, bias=True)
     Parameter containing:
     tensor([[1., 1.],
             [1., 1.]], requires_grad=True)
     Linear(in_features=2, out_features=2, bias=True)
     Parameter containing:
     tensor([[1., 1.],
             [1., 1.]], requires_grad=True)
     Sequential(
       (0): Linear(in_features=2, out_features=2, bias=True)
       (1): Linear(in_features=2, out_features=2, bias=True)
     )




bfloat16()
  Casts all floating point parameters and buffers to "bfloat16"
  datatype.

  Note:

    This method modifies the module in-place.

  Returns:
     self

  Return type:
     Module

buffers(recurse=True)
  Returns an iterator over module buffers.

  Parameters:
