docstr: Test the model on a single batch of samples.
  
  Args:
      x: Input data. Must be array-like.
      y: Target data. Must be array-like.
      sample_weight: Optional array of the same length as x, containing
          weights to apply to the model's loss for each sample.
          In the case of temporal data, you can pass a 2D array
          with shape `(samples, sequence_length)`, to apply a different
          weight to every timestep of every sample.
      return_dict: If `True`, loss and metric results are returned as a
          dict, with each key being the name of the metric. If `False`,
          they are returned as a list.
  
  Returns:
      A scalar loss value (when no metrics and `return_dict=False`),
      a list of loss and metric values
      (if there are metrics and `return_dict=False`), or a dict of
      metric and loss values (if `return_dict=True`).
code: def test_on_batch(
      self,
      x,
      y=None,
      sample_weight=None,
      return_dict=False,
  ):
      """Test the model on a single batch of samples.
  
      Args:
          x: Input data. Must be array-like.
          y: Target data. Must be array-like.
          sample_weight: Optional array of the same length as x, containing
              weights to apply to the model's loss for each sample.
              In the case of temporal data, you can pass a 2D array
              with shape `(samples, sequence_length)`, to apply a different
              weight to every timestep of every sample.
          return_dict: If `True`, loss and metric results are returned as a
              dict, with each key being the name of the metric. If `False`,
              they are returned as a list.
  
      Returns:
          A scalar loss value (when no metrics and `return_dict=False`),
          a list of loss and metric values
          (if there are metrics and `return_dict=False`), or a dict of
          metric and loss values (if `return_dict=True`).
      """
      raise NotImplementedError
  
imports: import platform
  import warnings
  import tree
  from keras import backend
  from keras import metrics
  from keras import ops
  from keras import optimizers
  from keras.optimizers.loss_scale_optimizer import LossScaleOptimizer
  from keras.saving import serialization_lib
  from keras.trainers.compile_utils import CompileLoss
  from keras.trainers.compile_utils import CompileMetrics
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.utils import traceback_utils
  from keras.utils import tracking
label_desc: Use image node_id 19 for calling the Trainer obj's underlying member method code with example usage: obj.test_on_batch(x, y, sample_weight, return_dict) without return types