code: def eval_func(
      self,
      closure: LossClosure,
      variables: Sequence[tf.Variable],
      tf_fun_args: Mapping[str, Any],
      compile: bool = True,
      allow_unused_variables: bool = False,
  ) -> Callable[[AnyNDArray], Tuple[AnyNDArray, AnyNDArray]]:
      first_call = True
  
      def _tf_eval(x: tf.Tensor) -> Tuple[tf.Tensor, tf.Tensor]:
          nonlocal first_call
  
          values = self.unpack_tensors(variables, x)
          self.assign_tensors(variables, values)
  
          if first_call:
              # Only check for unconnected gradients on the first function evaluation.
              loss, grads = _compute_loss_and_gradients(
                  closure, variables, tf.UnconnectedGradients.NONE
              )
              grads = self._filter_unused_variables(
                  variables, grads, allow_unused_variables
              )
              first_call = False
          else:
              loss, grads = _compute_loss_and_gradients(
                  closure, variables, tf.UnconnectedGradients.ZERO
              )
  
          return loss, self.pack_tensors(grads)
  
      if compile:
          # Re-use the same tf.function graph for calls to minimize, as long as the arguments
          # affecting the graph are the same. This can boost performance of use cases where
          # minimize is called repeatedly with the same model loss.
          key = (
              closure,
              tuple(id(v) for v in variables),
              frozenset(tf_fun_args.items()),
              allow_unused_variables,
          )
          if self.compile_cache_size > 0:
              if key not in self.compile_cache:
                  if len(self.compile_cache) >= self.compile_cache_size:
                      self.compile_cache.popitem(
                          last=False
                      )  # Remove the oldest entry.
                  self.compile_cache[key] = tf.function(
                      _tf_eval, **tf_fun_args
                  )
              _tf_eval = self.compile_cache[key]
          else:
              _tf_eval = tf.function(_tf_eval, **tf_fun_args)
  
      def _eval(x: AnyNDArray) -> Tuple[AnyNDArray, AnyNDArray]:
          loss, grad = _tf_eval(tf.convert_to_tensor(x))
          return loss.numpy().astype(np.float64), grad.numpy().astype(
              np.float64
          )
  
      return _eval
  
imports: import warnings
  from collections import OrderedDict
  from typing import Any, Callable, FrozenSet, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
  import numpy
  import scipy.optimize
  import tensorflow
  from scipy.optimize import OptimizeResult
  from ..base import AnyNDArray
  from ..monitor.base import Monitor
label_desc: Use image node_id 4 for calling the Scipy obj's underlying member method code with example usage: obj.eval_func(closure, variables, tf_fun_args, compile, allow_unused_variables) and returns: _eval, loss,  self, loss,  grad