code: def perceptron(x, ny, config, w_init=None, b_init=None):
      """
      A very standard linear Perceptron model.
      :param x: Input tensor.
      :param ny: Number of outputs.
      :param config: A config object.
      :param w_init: Weight initializer.
      :param b_init: Bias initializer.
      :return: The output of the perceptron model.
      """
      w_init = w_init or tf.compat.v1.random_normal_initializer(
          stddev=config.weight_stddev
      )
      b_init = b_init or tf.compat.v1.constant_initializer(0)
  
      with tf.compat.v1.variable_scope("perceptron"):
          nx = config.n_embed
          w = tf.compat.v1.get_variable(
              "w", [nx, ny], initializer=w_init
          )
          b = tf.compat.v1.get_variable("b", [ny], initializer=b_init)
          return tf.matmul(x, w) + b
  
imports: import functools
  import tensorflow
  from tensorflow_addons.text.crf import crf_log_likelihood
  from finetune.base_models.gpt.featurizer import attn, dropout, norm
  from finetune.util.shapes import shape_list, merge_leading_dims
  from finetune.optimizers.recompute_grads import recompute_grad
  from finetune.errors import FinetuneError
  from finetune.nn.activations import act_fns
  from finetune.nn.nn_utils import norm
  from tensorflow.python.framework import function
label_desc: Use image node_id 1 for calling a global function with example usage: perceptron(x, ny, config, w_init, b_init) and returns: unknown