code: def LinearActivation(
      d_input,
      d_output,
      bias=True,
      zero_bias_init=False,
      transposed=False,
      initializer=None,
      activation=None,
      activate=False,
      weight_norm=False,
      **kwargs,
  ):
      """
      generate activation module
      Args:
          d_input (int): input dimension
          d_output (int): output dimension
          bias (bool): bias
          zero_bias_init (bool): bias
          transposed (bool): transposed
          initializer (str): initializer
          activation (str): activation
          activate (str): activation
          weight_norm (bool): weight normalization
      return: a linear nn.Module with control over axes order, initialization, and activation
      """
  
      # Construct core module
      linear_cls = TransposedLinear if transposed else nn.Linear
      if activation == "glu":
          d_output *= 2
      linear = linear_cls(d_input, d_output, bias=bias, **kwargs)
  
      # Initialize weight
      if initializer is not None:
          get_initializer(initializer, activation)(linear.weight)
  
      # Initialize bias
      if bias and zero_bias_init:
          nn.init.zeros_(linear.bias)
  
      # Weight norm
      if weight_norm:
          linear = nn.utils.weight_norm(linear)
  
      if activate and activation is not None:
          activation = Activation(
              activation, dim=-2 if transposed else -1
          )
          linear = nn.Sequential(linear, activation)
      return linear
  
imports: from torch import nn
  from towhee.models.vis4mer.transposelinear import TransposedLinear
  from towhee.models.vis4mer.activation import Activation
  from towhee.models.vis4mer.get_initializer import get_initializer
label_desc: Use image node_id 1 for calling a global function with example usage: LinearActivation(d_input, d_output, bias, zero_bias_init, transposed, initializer, activation, activate, weight_norm) and returns: linear