class_docstr: # Transformer based classifier model
class_docstr_tok: ["#","Transformer","based","classifier","model"]
code: def forward(self, x: torch.Tensor):
      # Get the token embeddings with positional encodings
      x = self.src_embed(x)
      # Transformer encoder
      x = self.encoder(x, None)
      # Get logits for classification.
      #
      # We set the `[CLS]` token at the last position of the sequence.
      # This is extracted by `x[-1]`, where `x` is of
      # shape `[seq_len, batch_size, d_model]`
      x = self.generator(x[-1])
  
      # Return results
      # (second value is for state, since our trainer is used with RNNs also)
      return x, None
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/transformers/fnet/experiment.py
imports: import torch
  from torch import nn
  from labml import experiment
  from labml.configs import option
  from labml_helpers.module import Module
  from labml_nn.experiments.nlp_classification import NLPClassificationConfigs
  from labml_nn.transformers import Encoder
  from labml_nn.transformers import TransformerConfigs
label_desc: Use image node_id 2 for calling the TransformerClassifier obj's underlying member method code with example usage: obj.forward(x) and returns: x,  None