class_docstr: BiSeNet head of BiseNetV1 model
  
  Args:
      in_channels (int): The number of input channels in spatial path module.
      out_channels (int): The number of output channels in spatial path module.
      scale (int, float): The scale factor of interpolation.
class_docstr_tok: ["BiSeNet","head","of","BiseNetV1","model","Args",":","in_channels","(","int",")",":","The","number","of","input","channels","in","spatial","path","module",".","out_channels","(","int",")",":","The","number","of","output","channels","in","spatial","path","module",".","scale","(","int",",","float",")",":","The","scale","factor","of","interpolation","."]
code: def __init__(self, in_channels, out_channels, scale, is_aux=False):
      super().__init__()
      inner_channel = 128 if is_aux else 64
      self.conv_3x3 = layers.ConvBNReLU(
          in_channels,
          inner_channel,
          3,
          stride=1,
          padding=1,
          bias_attr=False,
      )
      self.conv_1x1 = nn.Conv2D(inner_channel, out_channels, 1)
      self.scale = scale
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new BiSeNetHead object from inherited base classes: nn with example: obj = BiSeNetHead(in_channels, out_channels, scale, is_aux)