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
code: def __init__(
      self,
      in_features: int,
      out_features: Optional[int] = None,
      n_head: int = 4,
      dropout_rate: float = 0,
  ):
      super().__init__()
  
      if out_features is None:
          out_features = in_features
  
      # Parametrize with the total number of features (needs to be divisible by two due to stacking)
      if out_features % 2 != 0:
          raise ValueError(
              f"Number of output features should be divisible by two, currently set to {out_features}"
          )
  
      self.transform_channel = torch.nn.Sequential(
          torch.nn.Linear(in_features, out_features // 2, bias=False),
          torch.nn.ReLU(),
      )
      self.transform_attend = torch.nn.Sequential(
          torch.nn.Linear(in_features, out_features // 2, bias=False),
          torch.nn.ReLU(),
      )
      self.attention = MultiHeadAttention(
          n_head=n_head,
          n_feat=out_features // 2,
          dropout_rate=dropout_rate,
      )
  
      logging.debug("Initialized %s with", self.__class__.__name__)
      logging.debug("\tin_features:  %d", in_features)
      logging.debug("\tout_features: %d", out_features)
      logging.debug("\tn_head:       %d", n_head)
      logging.debug("\tdropout_rate: %f", dropout_rate)
  
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 1 to create a new TransformAttendConcatenate object from inherited base classes: NeuralModule with example: obj = TransformAttendConcatenate(in_features, out_features, n_head, dropout_rate)