class_docstr: Wrapper to provide a common interfaces among different classifier modules
      
class_docstr_tok: ["Wrapper","to","provide","a","common","interfaces","among","different","classifier","modules"]
docstr: Train the classifier for one optimization step.
  
  :param X_tokens: Tokenized and embedded training example
  :type X_tokens: torch.int64
  :param label: Label of the training example
  :type label: torch.int64
  :param X_mask: Mask differentiating tokens vs not tokens
  :type X_mask: torch.FloatTensor
  :return: losses, classifier prediction logits
  :rtype: tuple
docstr_tok: ["Train","the","classifier","for","one","optimization","step",".",":","param","X_tokens",":","Tokenized","and","embedded","training","example",":","type","X_tokens",":","torch.int64",":","param","label",":","Label","of","the","training","example",":","type","label",":","torch.int64",":","param","X_mask",":","Mask","differentiating","tokens","vs","not","tokens",":","type","X_mask",":","torch.FloatTensor",":","return",":","losses",",","classifier","prediction","logits",":","rtype",":","tuple"]
code: def _train_one_step(self, X_tokens, label, X_mask):
      """Train the classifier for one optimization step.
  
      :param X_tokens: Tokenized and embedded training example
      :type X_tokens: torch.int64
      :param label: Label of the training example
      :type label: torch.int64
      :param X_mask: Mask differentiating tokens vs not tokens
      :type X_mask: torch.FloatTensor
      :return: losses, classifier prediction logits
      :rtype: tuple
      """
      self.opt.zero_grad()
      self.model.zero_grad()
  
      cls_predict_logits, _, _ = self.model(
          X_tokens, attention_mask=X_mask
      )  # dimensions: (batch_size, hidden_dim, sequence_length)
  
      sup_loss = torch.mean(self.loss_func(cls_predict_logits, label))
      losses = {"g_sup_loss": sup_loss.cpu().data}
      sup_loss.backward()
  
      # Clip the norm of the gradients to 1.0.
      # This is to help prevent the "exploding gradients" problem.
      # torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)
  
      self.opt.step()
      return losses, cls_predict_logits
  
imports: import os
  import logging
  import numpy
  import torch
  import torch.nn
  from torch.autograd import Variable
  from tqdm import tqdm
  from interpret_text.experimental.common.utils_introspective_rationale import generate_data
label_desc: Use image node_id 4 for calling the ClassifierWrapper obj's underlying member method code with example usage: obj._train_one_step(X_tokens, label, X_mask) and returns: losses,  cls_predict_logits