code: def copy_layer(
      layer: Layer,
      keep_bias: bool = True,
      name_template: bool = None,
      weights: list[Tensor] | list[np.ndarray] | None = None,
      reuse_symbolic_tensors: bool = True,
      **kwargs,
  ) -> Layer:
      """Copy a Keras layer.
  
      :param layer: A layer that should be copied.
      :param keep_bias: Keep a potential bias.
      :param weights: Weights to set in the new layer.
        Options: np tensors, symbolic tensors, or None,
        in which case the weights from old_layers are used.
      :param reuse_symbolic_tensors: If the weights of the
        old_layer are used copy the symbolic ones or copy
        the Numpy weights.
      :return: The new layer instance.
      """
      config = layer.get_config()
      if name_template is None:
          config["name"] = None
      else:
          config["name"] = name_template % config["name"]
      if hasattr(layer, "use_bias"):
          if keep_bias is False and config.get("use_bias", True):
              config["use_bias"] = False
              if weights is None:
                  if reuse_symbolic_tensors:
                      weights = layer.weights[:-1]
                  else:
                      weights = layer.get_weights()[:-1]
      return get_layer_from_config(
          layer, config, weights=weights, **kwargs
      )
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABCMeta, abstractmethod
  from typing import Any, Callable
  import numpy
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import tensorflow.keras.models
  import innvestigate.backend
  import innvestigate.backend.checks
  import innvestigate.layers
  from innvestigate.backend.types import Layer, LayerCheck, Model, NodeDict, OptionalList, ReverseTensorDict, Tensor
label_desc: Use image node_id 8 for calling a global function with example usage: copy_layer(layer, keep_bias, name_template, weights, reuse_symbolic_tensors) and returns: get_layer_from_config