code: def sigmoid_with_limit(t: int, limit: int) -> float:
      """Calculate a sigmoid that ramps up to 1 after a certain limit.
  
      This function calculates a sigmoid function, defined as `sigmoid(x)`, where
      `x = 10 * t / limit - 5`. This results in `x` ranging from -5 at 0 to 5 at
      `limit`, and thus the output starts from a value near 0 at `t = 0` and ramps
      up near 1 at `t = limit`.
  
      Args:
        t: The point at which to evaluate the sigmoid.
        limit: The point at which the sigmoid must attain the value 1-exp(-5),
          which is close enough to 1.
  
      Returns:
        The value of the sigmoid at the chosen point.
      """
      if limit > 0:
          result = tf.sigmoid(
              10.0 * tf.cast(t, tf.float32) / tf.cast(limit, tf.float32)
              - 5.0
          )
      else:
          result = 0.2
      return result
  
file_path: neural-structured-learning/research/meo/parameter_generator_generation/parameter_generator.py
imports: import os
  from typing import Optional, Sequence
  from absl import app
  from absl import flags
  from absl import logging
  from meo.mlp_baseline import configs
  from meo.mlp_baseline import data_utils
  from meo.mlp_baseline import extended_model
  from meo.mlp_baseline import losses
  from meo.mlp_baseline import obfuscations
  import numpy
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: sigmoid_with_limit(t, limit) and returns: result