class_docstr: Position-wise feed-forward network with activation.
  
  .. math::
  
      activation(XW_1 + b_1)W_2 + b_2
  
  :math:`W_1`: (batch_size, d, inner_dim)
  :math:`W_2`: (batch_size, inner_dim, out_dim)
class_docstr_tok: ["Position-wise","feed-forward","network","with","activation",".","..","math",":",":","activation","(","XW_1","+","b_1",")","W_2","+","b_2",":","math",":","`","W_1","`",":","(","batch_size",",","d",",","inner_dim",")",":","math",":","`","W_2","`",":","(","batch_size",",","inner_dim",",","out_dim",")"]
docstr: Position-wise feed-forward network with activation.
  
  Parameters
  ----------
  x
      Tensor of shape (batch_size, d, in_dim)
  
  Returns
  -------
  Tensor of shape (batch_size, d1, out_dim)
docstr_tok: ["Position-wise","feed-forward","network","with","activation",".","Parameters","--","--","--","--","--","x","Tensor","of","shape","(","batch_size",",","d",",","in_dim",")","Returns","--","--","--","-","Tensor","of","shape","(","batch_size",",","d1",",","out_dim",")"]
code: def hybrid_forward(self, F, x: Tensor, *args) -> Tensor:
      r"""
      Position-wise feed-forward network with activation.
  
      Parameters
      ----------
      x
          Tensor of shape (batch_size, d, in_dim)
  
      Returns
      -------
      Tensor of shape (batch_size, d1, out_dim)
      """
  
      return self.mlp(x)
  
imports: from typing import Dict, Optional, Tuple
  import mxnet
  from mxnet.gluon import HybridBlock
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the TransformerFeedForward obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: self