class_docstr: Class that contains a transform program and the methods to interact with it.
  
  The order of execution is the order in the list containing the containers.
  
  The main case where one would have to interact directly with a transform program is when developing a
  :class:`Device <pennylane.devices.Device>`. In this case, the pre-processing method of a device
  returns a transform program. You should directly refer to the device API documentation for more details.
  
  .. warning::
  
      This class is developer-facing and should not be used directly. Instead, use
      :func:`qml.transform <pennylane.transform>` if you would like to make a custom
      transform.
  
  .. seealso:: :func:`~.pennylane.transform`
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docstr: Add a transform (dispatcher) to the beginning of the program.
  
  Args:
      transform(TransformDispatcher): The transform to add to the front of the transform program.
      *targs: Any additional arguments that are passed to the transform.
  
  Keyword Args:
      **tkwargs: Any additional keyword arguments that are passed to the transform.
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code: def insert_front_transform(
      self, transform: TransformDispatcher, *targs, **tkwargs
  ):
      """Add a transform (dispatcher) to the beginning of the program.
  
      Args:
          transform(TransformDispatcher): The transform to add to the front of the transform program.
          *targs: Any additional arguments that are passed to the transform.
  
      Keyword Args:
          **tkwargs: Any additional keyword arguments that are passed to the transform.
  
      """
      if transform.final_transform and not self.is_empty():
          raise TransformError(
              "Informative transforms can only be added at the end of the program."
          )
  
      self.insert_front(
          TransformContainer(
              transform.transform,
              targs,
              tkwargs,
              transform.classical_cotransform,
              transform.is_informative,
              transform.final_transform,
          )
      )
  
      if transform.expand_transform:
          self.insert_front(
              TransformContainer(
                  transform.expand_transform, targs, tkwargs
              )
          )
  
imports: from functools import partial
  from typing import Callable, List, Tuple, Optional, Sequence
  import pennylane
  from pennylane.typing import Result, ResultBatch
  from pennylane.tape import QuantumTape
  from .transform_dispatcher import TransformContainer, TransformError, TransformDispatcher
label_desc: Use image node_id 12 for calling the TransformProgram obj's underlying member method code with example usage: obj.insert_front_transform(transform) without return types