class_docstr: Dense linear classifier.
  
  Args:
      in_features (int): size of each input sample.
      num_classes (int): number of classes.
      weight (:class:`torch.nn.Parameter`, optional): weight of the classifier, defaults to None.
      dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
      flatten (bool, optional): whether to flatten output tensor, defaults to True.
      weight_initializer (:class:`typing.Callable`, optional):
          The initializer of weight, defaults to kaiming uniform initializer.
      bias_initializer (:class:`typing.Callable`, optional):
          The initializer of bias, defaults to xavier uniform initializer.
  
  More details about initializer please refer to
  `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
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code: def forward(self, input_: Tensor) -> Tensor:
      return F.linear(input_, self.weight, self.bias)
  
imports: import math
  from typing import Callable
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch import nn
  from torch.nn.parameter import Parameter
  from colossalai.legacy.context import seed
  from colossalai.legacy.registry import LAYERS
  from colossalai.nn import init
  from colossalai.utils.device import get_current_device
  from ..utils import to_2tuple
label_desc: Use image node_id 3 for calling the VanillaClassifier obj's underlying member method code with example usage: obj.forward(input_) and returns: F