243 lines
9.7 KiB
Plaintext
243 lines
9.7 KiB
Plaintext
---
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title: L_BFGS_B
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description: API reference for qiskit.algorithms.optimizers.L_BFGS_B
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.optimizers.L_BFGS_B
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---
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# L\_BFGS\_B
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<Class id="qiskit.algorithms.optimizers.L_BFGS_B" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.20/qiskit/algorithms/optimizers/l_bfgs_b.py" signature="L_BFGS_B(maxfun=1000, maxiter=15000, ftol=2.220446049250313e-15, factr=None, iprint=- 1, epsilon=1e-08, eps=1e-08, options=None, max_evals_grouped=1, **kwargs)" modifiers="class">
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Bases: `qiskit.algorithms.optimizers.scipy_optimizer.SciPyOptimizer`
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Limited-memory BFGS Bound optimizer.
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The target goal of Limited-memory Broyden-Fletcher-Goldfarb-Shanno Bound (L-BFGS-B) is to minimize the value of a differentiable scalar function $f$. This optimizer is a quasi-Newton method, meaning that, in contrast to Newtons’s method, it does not require $f$’s Hessian (the matrix of $f$’s second derivatives) when attempting to compute $f$’s minimum value.
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Like BFGS, L-BFGS is an iterative method for solving unconstrained, non-linear optimization problems, but approximates BFGS using a limited amount of computer memory. L-BFGS starts with an initial estimate of the optimal value, and proceeds iteratively to refine that estimate with a sequence of better estimates.
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The derivatives of $f$ are used to identify the direction of steepest descent, and also to form an estimate of the Hessian matrix (second derivative) of $f$. L-BFGS-B extends L-BFGS to handle simple, per-variable bound constraints.
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Uses scipy.optimize.fmin\_l\_bfgs\_b. For further detail, please refer to [https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html](https://docs.scipy.org/doc/scipy/reference/optimize.minimize-lbfgsb.html)
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**Parameters**
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* **maxfun** (`int`) – Maximum number of function evaluations.
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* **maxiter** (`int`) – Maximum number of iterations.
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* **ftol** (`float`) – The iteration stops when (f^k - f^\{k+1})/max\{|f^k|,|f^\{k+1}|,1} \<= ftol.
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* **factr** (`Optional`\[`float`]) – (DEPRECATED) The iteration steps when (f^k - f^\{k+1})/max\{|f^k|, |f^\{k+1}|,1} \<= factr \* eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. See Notes for relationship to ftol, which is exposed (instead of factr) by the scipy.optimize.minimize interface to L-BFGS-B.
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* **iprint** (`int`) – Controls the frequency of output. iprint \< 0 means no output; iprint = 0 print only one line at the last iteration; 0 \< iprint \< 99 print also f and |proj g| every iprint iterations; iprint = 99 print details of every iteration except n-vectors; iprint = 100 print also the changes of active set and final x; iprint > 100 print details of every iteration including x and g.
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* **eps** (`float`) – If jac is approximated, use this value for the step size.
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* **epsilon** (`float`) – (DEPRECATED) Step size used when approx\_grad is True, for numerically calculating the gradient
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* **options** (`Optional`\[`dict`]) – A dictionary of solver options.
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* **max\_evals\_grouped** (`int`) – Max number of default gradient evaluations performed simultaneously.
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* **kwargs** – additional kwargs for scipy.optimize.minimize.
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## Methods
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### get\_support\_level
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.get_support_level" signature="L_BFGS_B.get_support_level()">
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Return support level dictionary
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</Function>
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### gradient\_num\_diff
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.gradient_num_diff" signature="L_BFGS_B.gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)" modifiers="static">
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We compute the gradient with the numeric differentiation in the parallel way, around the point x\_center.
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**Parameters**
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* **x\_center** (*ndarray*) – point around which we compute the gradient
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* **f** (*func*) – the function of which the gradient is to be computed.
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* **epsilon** (*float*) – the epsilon used in the numeric differentiation.
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* **max\_evals\_grouped** (*int*) – max evals grouped
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**Returns**
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the gradient computed
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**Return type**
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grad
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</Function>
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### minimize
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.minimize" signature="L_BFGS_B.minimize(fun, x0, jac=None, bounds=None)">
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Minimize the scalar function.
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**Parameters**
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* **fun** (`Callable`\[\[`Union`\[`float`, `ndarray`]], `float`]) – The scalar function to minimize.
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* **x0** (`Union`\[`float`, `ndarray`]) – The initial point for the minimization.
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* **jac** (`Optional`\[`Callable`\[\[`Union`\[`float`, `ndarray`]], `Union`\[`float`, `ndarray`]]]) – The gradient of the scalar function `fun`.
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* **bounds** (`Optional`\[`List`\[`Tuple`\[`float`, `float`]]]) – Bounds for the variables of `fun`. This argument might be ignored if the optimizer does not support bounds.
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**Return type**
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`OptimizerResult`
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**Returns**
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The result of the optimization, containing e.g. the result as attribute `x`.
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</Function>
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### optimize
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.optimize" signature="L_BFGS_B.optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)">
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Perform optimization.
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**Parameters**
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* **num\_vars** (*int*) – Number of parameters to be optimized.
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* **objective\_function** (*callable*) – A function that computes the objective function.
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* **gradient\_function** (*callable*) – A function that computes the gradient of the objective function, or None if not available.
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* **variable\_bounds** (*list\[(float, float)]*) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
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* **initial\_point** (*numpy.ndarray\[float]*) – Initial point.
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**Returns**
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**point, value, nfev**
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point: is a 1D numpy.ndarray\[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None
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**Raises**
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**ValueError** – invalid input
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</Function>
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### print\_options
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.print_options" signature="L_BFGS_B.print_options()">
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Print algorithm-specific options.
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</Function>
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### set\_max\_evals\_grouped
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.set_max_evals_grouped" signature="L_BFGS_B.set_max_evals_grouped(limit)">
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Set max evals grouped
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</Function>
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### set\_options
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.set_options" signature="L_BFGS_B.set_options(**kwargs)">
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Sets or updates values in the options dictionary.
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The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.
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**Parameters**
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**kwargs** (*dict*) – options, given as name=value.
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</Function>
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### wrap\_function
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<Function id="qiskit.algorithms.optimizers.L_BFGS_B.wrap_function" signature="L_BFGS_B.wrap_function(function, args)" modifiers="static">
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Wrap the function to implicitly inject the args at the call of the function.
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**Parameters**
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* **function** (*func*) – the target function
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* **args** (*tuple*) – the args to be injected
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**Returns**
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wrapper
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**Return type**
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function\_wrapper
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</Function>
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## Attributes
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### bounds\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.bounds_support_level">
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Returns bounds support level
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</Attribute>
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### gradient\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.gradient_support_level">
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Returns gradient support level
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</Attribute>
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### initial\_point\_support\_level
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.initial_point_support_level">
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Returns initial point support level
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</Attribute>
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### is\_bounds\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_ignored">
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Returns is bounds ignored
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</Attribute>
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### is\_bounds\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_required">
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Returns is bounds required
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</Attribute>
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### is\_bounds\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_bounds_supported">
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Returns is bounds supported
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</Attribute>
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### is\_gradient\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_ignored">
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Returns is gradient ignored
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</Attribute>
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### is\_gradient\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_required">
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Returns is gradient required
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</Attribute>
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### is\_gradient\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_gradient_supported">
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Returns is gradient supported
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</Attribute>
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### is\_initial\_point\_ignored
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_ignored">
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Returns is initial point ignored
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</Attribute>
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### is\_initial\_point\_required
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_required">
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Returns is initial point required
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</Attribute>
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### is\_initial\_point\_supported
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.is_initial_point_supported">
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Returns is initial point supported
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</Attribute>
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### setting
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.setting">
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Return setting
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</Attribute>
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### settings
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<Attribute id="qiskit.algorithms.optimizers.L_BFGS_B.settings">
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**Return type**
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`Dict`\[`str`, `Any`]
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</Attribute>
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</Class>
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