class_docstr: A baseclass for modules to perform various classification tasks.
class_docstr_tok: ["A","baseclass","for","modules","to","perform","various","classification","tasks","."]
docstr: Generates input examples for tracing etc.
  Returns:
      A tuple of input examples.
docstr_tok: ["Generates","input","examples","for","tracing","etc",".","Returns",":","A","tuple","of","input","examples","."]
code: def input_example(self, max_batch=1, max_dim=256):
      """
      Generates input examples for tracing etc.
      Returns:
          A tuple of input examples.
      """
      sample = next(self.parameters())
      example = (
          torch.randn(max_batch, max_dim, self._hidden_size)
          .to(sample.device)
          .to(sample.dtype)
      )
      return tuple([example])
  
file_path: NeMo/nemo/collections/nlp/modules/common/classifier.py
imports: from typing import Dict, Optional
  import torch
  from torch import nn
  from nemo.collections.common.parts import transformer_weights_init
  from nemo.core.classes import Exportable, NeuralModule
  from nemo.core.neural_types import ChannelType, NeuralType
label_desc: Use image node_id 4 for calling the Classifier obj's underlying member method code with example usage: obj.input_example(max_batch, max_dim) and returns: tuple