code: def __init__(
      self,
      in_features,
      out_features,
      bias_init_val,
      weight_attr=None,
      name=None,
  ):
      super().__init__()
      self._dtype = self._helper.get_default_dtype()
      self._weight_attr = weight_attr
      self.weight = self.create_parameter(
          shape=[in_features, out_features],
          attr=self._weight_attr,
          dtype=self._dtype,
          is_bias=False,
      )
      bias_init_val = bias_init_val.flatten()
      bias_init_val = bias_init_val.astype(self._dtype)
      if bias_init_val.shape[0] != out_features:
          raise ValueError
      self.register_buffer("bias", bias_init_val)
      self.name = name
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import math
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import param_init
  from paddlepanseg.models.param_init import THLinearInitMixin
  from paddlepanseg.utils import use_custom_op
label_desc: Use image node_id 1 to create a new LinearWithFrozenBias object from inherited base classes: nn with example: obj = LinearWithFrozenBias(in_features, out_features, bias_init_val, weight_attr, name)