code: def _bisect_compute_b(u: np.ndarray):
      """
      Given the U matrix, compute the B matrix such that
      H Bt x B x H = U
      where Bt is the adjoint of B,
      H is the Hadamard matrix,
      and x is the Pauli X matrix.
      """
      sqrt = np.sqrt
      Abs = np.abs
      w = np.real(u[0, 0])
      s = np.real(u[1, 0])
      t = np.imag(u[1, 0])
      if np.isclose(s, 0):
          b = 0
          if np.isclose(t, 0):
              if w < 0:
                  c = 0
                  d = sqrt(-w)
              else:
                  c = sqrt(w)
                  d = 0
          else:
              c = sqrt(2 - 2 * w) * (-w / 2 - 1 / 2) / t
              d = sqrt(2 - 2 * w) / 2
      elif np.isclose(t, 0):
          b = (1 / 2 - w / 2) * sqrt(2 * w + 2) / s
          c = sqrt(2 * w + 2) / 2
          d = 0
      else:
          b = (
              sqrt(2)
              * s
              * sqrt((1 - w) / (s**2 + t**2))
              * Abs(t)
              / (2 * t)
          )
          c = (
              sqrt(2)
              * sqrt((1 - w) / (s**2 + t**2))
              * (w + 1)
              * Abs(t)
              / (2 * t)
          )
          d = -sqrt(2) * sqrt((1 - w) / (s**2 + t**2)) * Abs(t) / 2
      return _param_su2(c, d, b, 0)
  
file_path: pennylane/pennylane/ops/op_math/controlled_decompositions.py
imports: from copy import copy
  import numpy
  import numpy.linalg
  import pennylane
  from pennylane.operation import Operator
  from pennylane.wires import Wires
  from pennylane import math
label_desc: Use image node_id 5 for calling a global function with example usage: _bisect_compute_b(u) and returns: _param_su2