class_docstr: Apply transform-attend-concatenate across channels.
  The output is a concatenation of transformed channel and MHA
  over channels.
  
  Args:
      in_features: Number of input features
      out_features: Number of output features
      n_head: Number of heads for the MHA module
      dropout_rate: Dropout rate for the MHA module
  
  References:
      - Jukić et al, Flexible multichannel speech enhancement for noise-robust frontend, 2023
docstr: Returns definitions of module output types
          
docstr_tok: ["Returns","definitions","of","module","output","types"]
code: def output_types(self):
      """Returns definitions of module output types"""
      return {
          "output": NeuralType(("B", "C", "D", "T"), SpectrogramType()),
      }
  
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 3 for calling the TransformAttendConcatenate obj's underlying member method code with example usage: obj.output_types() and returns: dict