def _call_se(self, input_tensor):
    """Call Squeeze and Excitation layer.

    Args:
      input_tensor: Tensor, a single input tensor for Squeeze/Excitation layer.

    Returns:
      A output tensor, which should have the same shape as input.
    """
    se_tensor = tf.reduce_mean(input_tensor, self._spatial_dims, keepdims=True)
    se_tensor = self._se_expand(self._relu_fn(self._se_reduce(se_tensor)))
    logging.info('Built Squeeze and Excitation with tensor shape: %s',
                 (se_tensor.shape))
    return tf.sigmoid(se_tensor) * input_tensor
Call Squeeze and Excitation layer.

    Args:
      input_tensor: Tensor, a single input tensor for Squeeze/Excitation layer.

    Returns:
      A output tensor, which should have the same shape as input.
    
tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_model.py