class_docstr: Squeeze-and-excitation block.
  
  Defined: https://arxiv.org/abs/1709.01507
  
  Arguments
  ---------
  input_size : tuple of ints
      Expected size of the input tensor
  
  Example
  -------
  >>> inputs = torch.rand([10, 20, 30, 256])
  >>> se_block = SEblock(input_size=inputs.shape[-1])
  >>> outputs = se_block(inputs)
  >>> outputs.shape
  torch.Size([10, 1, 1, 256])
code: def __init__(self, input_size):
      super().__init__()
      self.linear1 = sb.nnet.linear.Linear(
          input_size=input_size, n_neurons=input_size
      )
      self.linear2 = sb.nnet.linear.Linear(
          input_size=input_size, n_neurons=input_size
      )
  
file_path: speechbrain/speechbrain/lobes/models/EnhanceResnet.py
imports: import torch
  import speechbrain
  from speechbrain.processing.features import STFT, ISTFT, spectral_magnitude
label_desc: Use image node_id 1 to create a new SEblock object from inherited base classes: torch.nn with example: obj = SEblock(input_size)