docstr: Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients
  :param additional_fetches: Optional tensors to fetch during the training process
  :param inputs: The input for the network
  :param targets: The targets corresponding to the input batch
  :param scaler: A scaling factor that allows rescaling the gradients before applying them
  :param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
                             error of this sample. If it is not given, the samples losses won't be scaled
  :return: The loss of the network
code: def train_on_batch(
      self,
      inputs,
      targets,
      scaler=1.0,
      additional_fetches=None,
      importance_weights=None,
  ):
      """
      Given a batch of examples and targets, runs a forward pass & backward pass and then applies the gradients
      :param additional_fetches: Optional tensors to fetch during the training process
      :param inputs: The input for the network
      :param targets: The targets corresponding to the input batch
      :param scaler: A scaling factor that allows rescaling the gradients before applying them
      :param importance_weights: A coefficient for each sample in the batch, which will be used to rescale the loss
                                 error of this sample. If it is not given, the samples losses won't be scaled
      :return: The loss of the network
      """
      if additional_fetches is None:
          additional_fetches = []
      force_list(additional_fetches)
      loss = self.accumulate_gradients(
          inputs,
          targets,
          additional_fetches=additional_fetches,
          importance_weights=importance_weights,
      )
      self.apply_and_reset_gradients(self.accumulated_gradients, scaler)
      return loss
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 19 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.train_on_batch(inputs, targets, scaler, additional_fetches, importance_weights) and returns: loss