class_docstr: Randomly permute and selects a subset of channels.
  
  Args:
      permute_channels (bool): Apply a random permutation of channels.
      num_channels_min (int): Minimum number of channels to select.
      num_channels_max (int): Max number of channels to select.
      rng: Optional, random generator.
      seed: Optional, seed for the generator.
docstr: Returns definitions of module input types
          
docstr_tok: ["Returns","definitions","of","module","input","types"]
code: def input_types(self):
      """Returns definitions of module input types"""
      return {
          "input": NeuralType(("B", "C", "T"), AudioSignal()),
      }
  
imports: import random
  from typing import Callable, Optional
  import torch
  from nemo.collections.asr.parts.submodules.multi_head_attention import MultiHeadAttention
  from nemo.core.classes import NeuralModule, typecheck
  from nemo.core.neural_types import AudioSignal, FloatType, NeuralType, SpectrogramType
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the ChannelAugment obj's underlying member method code with example usage: obj.input_types() and returns: dict