class_docstr: Simulator plugin based on ``"default.qubit.legacy"``, written using TensorFlow.
  
  **Short name:** ``default.qubit.tf``
  
  This device provides a pure-state qubit simulator written using TensorFlow.
  As a result, it supports classical backpropagation as a means to compute the Jacobian. This can
  be faster than the parameter-shift rule for analytic quantum gradients
  when the number of parameters to be optimized is large.
  
  To use this device, you will need to install TensorFlow:
  
  .. code-block:: console
  
      pip install tensorflow>=2.0
  
  **Example**
  
  The ``default.qubit.tf`` is designed to be used with end-to-end classical backpropagation
  (``diff_method="backprop"``) with the TensorFlow interface. This is the default method
  of differentiation when creating a QNode with this device.
  
  Using this method, the created QNode is a 'white-box', and is
  tightly integrated with your TensorFlow computation:
  
  >>> dev = qml.device("default.qubit.tf", wires=1)
  >>> @qml.qnode(dev, interface="tf", diff_method="backprop")
  ... def circuit(x):
  ...     qml.RX(x[1], wires=0)
  ...     qml.Rot(x[0], x[1], x[2], wires=0)
  ...     return qml.expval(qml.PauliZ(0))
  >>> weights = tf.Variable([0.2, 0.5, 0.1])
  >>> with tf.GradientTape() as tape:
  ...     res = circuit(weights)
  >>> print(tape.gradient(res, weights))
  tf.Tensor([-2.2526717e-01 -1.0086454e+00  1.3877788e-17], shape=(3,), dtype=float32)
  
  Autograph mode will also work when using classical backpropagation:
  
  >>> @tf.function
  ... def cost(weights):
  ...     return tf.reduce_sum(circuit(weights)**3) - 1
  >>> with tf.GradientTape() as tape:
  ...     res = cost(weights)
  >>> print(tape.gradient(res, weights))
  tf.Tensor([-3.5471588e-01 -1.5882589e+00  3.4694470e-17], shape=(3,), dtype=float32)
  
  There are a couple of things to keep in mind when using the ``"backprop"``
  differentiation method for QNodes:
  
  * You must use the ``"tf"`` interface for classical backpropagation, as TensorFlow is
    used as the device backend.
  
  * Only exact expectation values, variances, and probabilities are
    differentiable. Creation of a backpropagation QNode with finite shots
    raises an error. If you do try and take a derivative with finite shots on
    this device, the gradient will be ``None``.
  
  
  If you wish to use a different machine-learning interface, or prefer to calculate quantum
  gradients using the ``parameter-shift`` or ``finite-diff`` differentiation methods,
  consider using the ``default.qubit`` device instead.
  
  
  Args:
      wires (int, Iterable[Number, str]): Number of subsystems represented by the device,
          or iterable that contains unique labels for the subsystems as numbers (i.e., ``[-1, 0, 2]``)
          or strings (``['ancilla', 'q1', 'q2']``). Default 1 if not specified.
      shots (None, int): How many times the circuit should be evaluated (or sampled) to estimate
          the expectation values. Defaults to ``None`` if not specified, which means
          that the device returns analytical results.
          If ``shots > 0`` is used, the ``diff_method="backprop"``
          QNode differentiation method is not supported and it is recommended to consider
          switching device to ``default.qubit`` and using ``diff_method="parameter-shift"``.
code: def capabilities(cls):
      capabilities = super().capabilities().copy()
      capabilities.update(passthru_interface="tf")
      return capabilities
  
file_path: pennylane/pennylane/devices/default_qubit_tf.py
imports: import itertools
  import numpy
  import semantic_version
  import pennylane
  from pennylane.math.single_dispatch import _ndim_tf
  from .None import DefaultQubitLegacy
  from .default_qubit_legacy import tolerance
label_desc: Use image node_id 4 for calling the DefaultQubitTF obj's underlying member method code with example usage: obj.capabilities(cls) and returns: capabilities