class_docstr: Applies a linear transformation together with :class:`~torch.nn.LeakyReLU` activation to the incoming tensor:
  :math:`y = \mathrm{LeakyReLU}(x A^T + b)`
  
  Args:
      n_in (~torch.Tensor):
          The size of each input feature.
      n_out (~torch.Tensor):
          The size of each output feature.
      dropout (float):
          If non-zero, introduce a :class:`SharedDropout` layer on the output with this dropout ratio. Default: 0.
class_docstr_tok: ["Applies","a","linear","transformation","together","with",":","class",":","`","~torch.nn.LeakyReLU","`","activation","to","the","incoming","tensor",":",":","math",":","`","y","=","\\mathrm","{","LeakyReLU","}","(","x","A^T","+","b",")","`","Args",":","n_in","(","~torch.Tensor",")",":","The","size","of","each","input","feature",".","n_out","(","~torch.Tensor",")",":","The","size","of","each","output","feature",".","dropout","(","float",")",":","If","non-zero",",","introduce","a",":","class",":","`","SharedDropout","`","layer","on","the","output","with","this","dropout","ratio",".","Default",":","0","."]
docstr: Args:
      x (~torch.Tensor):
          The size of each input feature is `n_in`.
  
  Returns:
      A tensor with the size of each output feature `n_out`.
docstr_tok: ["Args",":","x","(","~torch.Tensor",")",":","The","size","of","each","input","feature","is","`","n_in","`",".","Returns",":","A","tensor","with","the","size","of","each","output","feature","`","n_out","`","."]
code: def forward(self, x):
      r"""
      Args:
          x (~torch.Tensor):
              The size of each input feature is `n_in`.
  
      Returns:
          A tensor with the size of each output feature `n_out`.
      """
  
      x = self.linear(x)
      x = self.activation(x)
      x = self.dropout(x)
  
      return x
  
imports: import torch
  import torch.nn
  from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
  from torch.nn.modules.rnn import apply_permutation
label_desc: Use image node_id 4 for calling the MLP obj's underlying member method code with example usage: obj.forward(x) and returns: x