code: def get_variable(interface, wire_specs, complex=False):
      """Return an interface-specific trainable variable"""
      num_wires = len(wire_specs[1])
  
      if interface is None:
          return np.array([0.1] * num_wires)
      if interface == "autograd":
          return np.array([0.1] * num_wires, requires_grad=True)
      if interface == "jax":
          # complex dtype is required for JAX when holomorphic gradient is used
          return jnp.array(
              [0.1] * num_wires,
              dtype=np.complex128 if complex else None,
          )
      if interface == "tf":
          # complex dtype is required for TF when the gradients have non-zero
          # imaginary parts, otherwise they will be ignored
          return tf.Variable(
              [0.1] * num_wires,
              trainable=True,
              dtype=tf.complex128 if complex else tf.float64,
          )
      if interface == "torch":
          # complex dtype is required for torch when the gradients have non-zero
          # imaginary parts, otherwise they will be ignored
          return torch.tensor(
              [0.1] * num_wires,
              requires_grad=True,
              dtype=torch.complex64 if complex else None,
          )
      return None
  
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane import QuantumFunctionError
  from pennylane.measurements import Probability, Expectation, Variance, Sample, VnEntropy, MutualInfo
  from pennylane.measurements.measurements import ObservableReturnTypes
label_desc: Use image node_id 2 for calling a global function with example usage: get_variable(interface, wire_specs, complex) and returns: None, np, np, jnp, tf, torch