Merge branch 'Classiq:main' into trial_1

Author: PhiloMathysicist

README.md

@@ -19,9 +19,9 @@ This repository holds a wide collection of quantum functions, algorithms, applic
     | 
    <a href="https://short.classiq.io/join-slack">👋 Join Slack</a>
    &emsp;|&emsp;
-   <a href="https://docs.classiq.io/latest/user-guide/">📖 Documentation</a>
+   <a href="https://docs.classiq.io/latest/">📖 Documentation</a>
    &emsp; | &emsp;
-   <a href="https://docs.classiq.io/latest/getting-started/">Getting Started</a>
+   <a href="https://docs.classiq.io/latest/">Getting Started</a>
    &emsp;
 </p>
 
@@ -95,13 +95,13 @@ def main(res: Output[QBit]):
     X(res)
 ```
 
-The 1st line states that the function will be a quantum one. [Further documentation](https://docs.classiq.io/latest/user-guide/platform/qmod/language-reference/functions/).
+The 1st line states that the function will be a quantum one. [Further documentation](https://docs.classiq.io/latest/reference-manual/platform/qmod/language-reference/functions/).
 
-The 2nd line defines the type of the output. [Further examples on types](https://docs.classiq.io/latest/user-guide/platform/qmod/language-reference/quantum-types/)
+The 2nd line defines the type of the output. [Further examples on types](https://docs.classiq.io/latest/reference-manual/platform/qmod/language-reference/classical-types/)
 
-The 3rd line allocates several qubits (in this example, only 1) in this quantum variable. [Further details on allocate](https://docs.classiq.io/latest/user-guide/platform/qmod/language-reference/quantum-variables/)
+The 3rd line allocates several qubits (in this example, only 1) in this quantum variable. [Further details on allocate](https://docs.classiq.io/latest/reference-manual/platform/qmod/language-reference/quantum-variables/)
 
-The 4th line applies an `X` operator on the quantum variable. [Further details on quantum operators](https://docs.classiq.io/latest/user-guide/platform/qmod/language-reference/operators/)
+The 4th line applies an `X` operator on the quantum variable. [Further details on quantum operators](https://docs.classiq.io/latest/reference-manual/platform/qmod/language-reference/operators/)
 
 ### More Examples
 
@@ -168,17 +168,17 @@ Classiq provides an easy-to-use way to execute quantum programs, and provides va
 flowchart
     IDEInput[<a href='https://platform.classiq.io/'>Classiq IDE</a>]
 
-    SDKInput[<a href='https://docs.classiq.io/latest/getting-started/python-sdk/'>Classiq python SDK</a>]
+    SDKInput[<a href='https://docs.classiq.io/latest/reference-manual/python-sdk/'>Classiq python SDK</a>]
 
-    Model[<a href='https://docs.classiq.io/latest/user-guide/platform/qmod/'>Quantum Model</a>]
+    Model[<a href='https://docs.classiq.io/latest/reference-manual/platform/qmod/'>Quantum Model</a>]
 
-    Synthesis[<a href='https://docs.classiq.io/latest/user-guide/platform/synthesis/'>Synthesis Engine</a>]
+    Synthesis[<a href='https://docs.classiq.io/latest/classiq_101/classiq_concepts/optimize/'>Synthesis Engine</a>]
 
     QuantumProgram[Quantum Program]
 
-    Execution[<a href='https://docs.classiq.io/latest/user-guide/platform/executor/'>Execution</a>]
+    Execution[<a href='https://docs.classiq.io/latest/classiq_101/classiq_concepts/execute/'>Execution</a>]
 
-    Analyze[<a href='https://docs.classiq.io/latest/user-guide/platform/analyzer/'>Analyze & Debug</a>]
+    Analyze[<a href='https://docs.classiq.io/latest/classiq_101/classiq_concepts/analyze/'>Analyze & Debug</a>]
 
     IBM[IBM]
     Amazon[Amazon Braket]
@@ -201,7 +201,7 @@ flowchart
 
 # Build Your Own
 
-With Classiq, you can build anything. Classiq provides a powerful modeling language to describe any quantum program, which can then be synthesized and executed on any hardware or simulator. Explore our [Documentation](https://docs.classiq.io/latest/user-guide/) to learn everything.
+With Classiq, you can build anything. Classiq provides a powerful modeling language to describe any quantum program, which can then be synthesized and executed on any hardware or simulator. Explore our [Documentation](https://docs.classiq.io/latest/) to learn everything.
 
 ## SDK : Classiq's Python Interface
 

algorithms/algebraic/shor/doubly_controlled_modular_adder.qmod

@@ -7,7 +7,7 @@ qfunc phase_lad<value: int>(phi_b: qbit[]) {
 qfunc my_qft_step(qbv: qbit[]) {
   H(qbv[0]);
   repeat (index: qbv.len - 1) {
-    CPHASE<pi / (2 ** (index + 1))>(qbv[0], qbv[(index) + 1]);
+    CPHASE<pi / (2 ** (index + 1))>(qbv[0], qbv[index + 1]);
   }
 }
 

algorithms/algebraic/shor/shor_modular_exponentiation.qmod

@@ -7,7 +7,7 @@ qfunc phase_lad<value: int>(phi_b: qbit[]) {
 qfunc my_qft_step(qbv: qbit[]) {
   H(qbv[0]);
   repeat (index: qbv.len - 1) {
-    CPHASE<pi / (2 ** (index + 1))>(qbv[0], qbv[(index) + 1]);
+    CPHASE<pi / (2 ** (index + 1))>(qbv[0], qbv[index + 1]);
   }
 }
 

algorithms/amplitude_estimation/quantum_counting/quantum_counting_iqae.qmod

@@ -51,9 +51,9 @@ qfunc main<k: int>(output ind_reg: qbit) {
   full_reg: qbit[];
   allocate<5>(full_reg);
   my_iqae_algorithm<k, lambda(arg0) {
-    Z(arg0[(arg0.len) - 1]);
+    Z(arg0[arg0.len - 1]);
   }, lambda(arg0) {
-    iqae_state_preparation(arg0[0:2], arg0[2:(arg0.len) - 1], arg0[(arg0.len) - 1]);
+    iqae_state_preparation(arg0[0:2], arg0[2:arg0.len - 1], arg0[arg0.len - 1]);
   }>(full_reg);
   state_reg: qbit[4];
   full_reg -> {state_reg, ind_reg};

algorithms/bernstein_vazirani/bernstein_vazirani.ipynb

@@ -58,7 +58,7 @@
    "metadata": {},
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/bernstein_vazirani_algorithm.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/bernstein_vazirani_algorithm.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Figure 1. The Bernstein-Vazirani algorithm comprises three quantum blocks. The main part of the algorithm\n",
     "is the implementation of the Bernstein-Vazirani predicate $f(x)\\equiv (x\\cdot a) \\mod 2$. </figcaption>\n",
     "</center>"
@@ -130,7 +130,7 @@
    "metadata": {},
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/bernstein-vazirani_predicate.png\" style=\"width:80%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/bernstein-vazirani_predicate.png\" style=\"width:80%\">\n",
     "<figcaption align = \"middle\"> Figure 1. The Bernstein Vazirani Predicate $f(x)$ for a secret string $a=01101$, on $n$ qubits.\n",
     "The quantum variable $x$ is stored on the 5 upper qubits and the resulting value of $f$ is on the lower-most qubit.</figcaption>\n",
     "</center>"
@@ -384,7 +384,7 @@
    "source": [
     "## References\n",
     "\n",
-    "<a id='BVWiki'>[1]</a>: [Bernstein\u2013Vazirani (Wikipedia)](https://en.wikipedia.org/wiki/Bernstein%E2%80%93Vazirani_algorithm)\n",
+    "<a id='BVWiki'>[1]</a>: [Bernstein–Vazirani (Wikipedia)](https://en.wikipedia.org/wiki/Bernstein%E2%80%93Vazirani_algorithm)\n",
     "\n",
     "<a id='ssBV'>[2]</a>: [Pokharel B., and Daniel A. L. \"Demonstration of algorithmic quantum speedup.\" Physical Review Letters 130, 210602 (2023)](https://arxiv.org/abs/2207.07647)"
    ]

algorithms/deutsch_jozsa/deutsch_jozsa.ipynb

@@ -60,7 +60,7 @@
    "metadata": {},
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/deutsch_josza_closed.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/deutsch_josza_closed.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Figure 1. The Deutsch-Jozsa algorithm </figcaption>\n",
     "</center>"
    ]
@@ -407,7 +407,7 @@
    "metadata": {},
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/deutsch_jozsa_opened.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/deutsch_jozsa_opened.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Figure 2. The Deutsch-Jozsa algorithm for the complex example, focusing on oracle implementation </figcaption>\n",
     "</center>"
    ]

algorithms/grover/3_sat_grover/3_sat_grover.qmod

@@ -1,4 +1,4 @@
-qfunc expr_predicate(x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, res: qbit) {
+qfunc expr_predicate(x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, res: qbit[1]) {
   res ^= (x1 or x2 or x3) and ((not x1) or x2 or x3) and ((not x1) or (not x2) or (not x3)) and ((not x1) or (not x2) or x3) and (x1 or x2 or (not x3)) and ((not x1) or x2 or (not x3));
 }
 

algorithms/grover/3_sat_grover_large/3_sat_grover_large.qmod

@@ -1,4 +1,4 @@
-qfunc expr_predicate(x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, x4: qnum<1, False, 0>, res: qbit) {
+qfunc expr_predicate(x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, x4: qnum<1, False, 0>, res: qbit[1]) {
   res ^= (x2 or x3 or x4) and ((not x1) or x2 or x3) and ((not x1) or x2 or (not x3)) and ((not x1) or (not x2) or x3) and (x1 or (not x2) or (not x3)) and (x1 or (not x2) or x3) and ((not x1) or (not x2) or (not x4)) and ((not x1) or (not x2) or x4) and ((not x2) or (not x3) or (not x4)) and (x2 or (not x3) or x4) and (x1 or (not x3) or x4) and (x1 or (not x2) or (not x4)) and ((not x1) or (not x2) or (not x3));
 }
 

algorithms/grover/grover_max_cut/grover_max_cut.qmod

@@ -1,4 +1,4 @@
-qfunc expr_predicate(x0: qnum<1, False, 0>, x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, x4: qnum<1, False, 0>, res: qbit) {
+qfunc expr_predicate(x0: qnum<1, False, 0>, x1: qnum<1, False, 0>, x2: qnum<1, False, 0>, x3: qnum<1, False, 0>, x4: qnum<1, False, 0>, res: qbit[1]) {
   res ^= ((((((((((((2 * x0) * x1) + ((2 * x0) * x2)) - (2 * x0)) + ((2 * x1) * x2)) + ((2 * x1) * x3)) - (3 * x1)) + ((2 * x2) * x4)) - (3 * x2)) + ((2 * x3) * x4)) - (2 * x3)) - (2 * x4)) <= (-4);
 }
 

algorithms/hhl/hhl/hhl.qmod

@@ -21,10 +21,10 @@ qfunc my_matrix_inversion_flexible<unitary_with_power: qfunc <arg0: int>(arg1: q
 
 qfunc main(output res: qbit[floor(log(4, 2))], output indicator: qbit) {
   prepare_amplitudes<[
-    0.18257418583505536,
-    0.3651483716701107,
-    0.7302967433402214,
-    0.5477225575051661
+    0.1825741858,
+    0.3651483717,
+    0.7302967433,
+    0.5477225575
   ], 0.0>(res);
   my_matrix_inversion_flexible<lambda<pw>(arg1) {
     suzuki_trotter1_with_power_logic<[
@@ -34,19 +34,19 @@ qfunc main(output res: qbit[floor(log(4, 2))], output indicator: qbit) {
       },
       PauliTerm {
         pauli=[Pauli::I, Pauli::Z],
-        coefficient=-0.05249999999999999
+        coefficient=-0.0525
       },
       PauliTerm {
         pauli=[Pauli::I, Pauli::X],
-        coefficient=-0.030000000000000002
+        coefficient=-0.03
       },
       PauliTerm {
         pauli=[Pauli::Z, Pauli::I],
-        coefficient=-0.017499999999999988
+        coefficient=-0.0175
       },
       PauliTerm {
         pauli=[Pauli::Z, Pauli::Z],
-        coefficient=-0.057499999999999996
+        coefficient=-0.0575
       },
       PauliTerm {
         pauli=[Pauli::Z, Pauli::X],
@@ -58,7 +58,7 @@ qfunc main(output res: qbit[floor(log(4, 2))], output indicator: qbit) {
       },
       PauliTerm {
         pauli=[Pauli::X, Pauli::Z],
-        coefficient=0.045000000000000005
+        coefficient=0.045
       },
       PauliTerm {
         pauli=[Pauli::X, Pauli::X],
@@ -68,6 +68,6 @@ qfunc main(output res: qbit[floor(log(4, 2))], output indicator: qbit) {
         pauli=[Pauli::Y, Pauli::Y],
         coefficient=-0.06
       }
-    ], pw, 4, 1.8, -6.283185307179586>(arg1);
+    ], pw, 4, 1.8, -6.2831853072>(arg1);
   }, 4>(res, indicator);
 }

algorithms/qml/qgan/qgan_bars_and_strips.ipynb

@@ -899,7 +899,7 @@
    },
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/qgan_circuit.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/qgan_circuit.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Hierarchical view of the quantum circuit for the QGAN generator. The circuit consists of an angle encoding layer, an ansatz layer, and a post-processing layer </figcaption>\n",
     "</center>"
    ]
@@ -916,7 +916,7 @@
    },
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/qgan_angle_encoder.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/qgan_angle_encoder.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Angle encoding layer consists of two consecutive noncommuting rotations encoding a single datum. </figcaption>\n",
     "</center>"
    ]
@@ -933,7 +933,7 @@
    },
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/qgan_anzats.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/qgan_anzats.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Ansatz layer including parametrized rotation followed by a pair-wise entangler via RZZ gate sequence. </figcaption>\n",
     "</center>"
    ]
@@ -1153,7 +1153,7 @@
    },
    "source": [
     "<center>\n",
-    "<img src=\"https://classiq-docs-images.s3.amazonaws.com/qgan_training.png\" style=\"width:100%\">\n",
+    "<img src=\"https://docs.classiq.io/resources/qgan_training.png\" style=\"width:100%\">\n",
     "<figcaption align = \"middle\"> Example of two training sessions. The first (green) line depicts a process in which the loss function estimator is raising, a clear indication that the learning session does not seem to converge to the desired result. The second (orange) shows the convergence of both. The two components compete to improve their performance. </figcaption>\n",
     "</center>"
    ]

algorithms/qml/qsvm/qsvm.qmod

@@ -4,7 +4,6 @@ qfunc main(output qbv: qbit[]) {
 }
 
 cscope ```
-
 qsvm_results = qsvm_full_run(
     train_data=[[2.112186432983832, 0.44678210208296076], [1.4302388641836115, 2.0500863077201803], [1.7195292520528696, 2.155495109429753], [2.350606859600729, 1.3521944997967792], [2.1639392550749834, 2.858154321993804], [0.7415213200617944, 0.9960941727006177], [1.7052044687627945, 1.746156268650723], [1.154481655851151, 1.6296850849870834], [1.178550834103221, 2.4896666171792288], [1.4354595109691861, 2.4579563949501426], [1.302182045580439, 1.8007507228484583], [1.9572970134814875, 1.9123582698010868], [1.6106791225734967, 1.2793841361849436], [1.290997768246414, 0.7328787758880366], [1.4520918763727126, 1.5493209353369237], [1.3969974236871332, 1.8014514945519902], [1.977199796578803, 2.3384223503638606], [1.1275811281170995, 2.324466934392206], [1.3866941108254505, 1.529613601296024], [0.8031123903491375, 1.5721256347123322], [2.6790428274664535, 4.987426996112025], [2.4933103414105124, 5.14388063922973], [2.3501842146874936, 4.917084078977774], [4.178707007615895, 4.460515918834162], [2.948493219124173, 5.4123702911268765], [2.984282159194407, 5.1227667518958695], [2.5769216299750366, 4.508558046516978], [3.592064487915955, 5.555048347762887], [2.396370386221024, 4.870008037808379], [3.0969491698930156, 5.34962991789807], [2.755586029403162, 5.688202501088769], [3.396770558929631, 5.388148675378706], [2.9479167412155634, 5.8933575622716505], [2.613003217938794, 4.706244309823174], [3.1892281958306725, 5.524828040588774], [3.4174852452652087, 4.9812521745298435], [3.236577921191584, 4.888258645409059], [3.354555258869891, 5.034232387617792], [2.886158263159518, 5.167880953936314], [3.0219487270064307, 5.351739891114873]],
     train_labels=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
@@ -13,5 +12,4 @@ qsvm_results = qsvm_full_run(
     predict_data=[[0.9811440346538475, 1.5462800543732582], [2.1949262869084927, 1.0358013632909908], [1.7048706634628803, 1.4014678085835548], [1.2739358806925953, 0.6115754338877866], [0.6174425005849159, 2.0358927355135936], [1.6523417482007734, 0.9222481821966263], [0.7300525863052175, 1.1710853402857748], [0.4577747116111941, 1.7493595496304872], [1.8241751561761508, 1.3534955118043435], [1.3886315659624247, 2.494784307703368], [1.6905896718904094, 1.0768485551382758], [1.3895240864715106, 1.8568024446745914], [1.6585755659513455, 1.7036356008862523], [0.9046474633499264, 1.1392302611294014], [0.7130388391077676, 1.984915340265877], [1.5709575191408134, 1.637503844388876], [0.8284554279130794, 2.109172285606834], [0.8853853711248996, 2.135029086981866], [1.0023940082413283, 2.8048949846403453], [1.2388998395125284, 2.391540984718196], [3.018728391028778, 4.845684719753612], [3.083629721212037, 5.1506663321503074], [3.7531491079435564, 5.091859232676953], [3.5309511753612988, 5.564397956886731], [3.523385073048796, 4.553258712777688], [2.394841287844583, 5.355288217100166], [3.191156247800532, 5.401910488982688], [2.8343038553971556, 5.439747738100138], [3.343281609521619, 5.024999185517319], [3.1542441151785208, 5.204696355131997], [3.658408764046296, 4.611626765508735], [3.3619284175956508, 5.0769972783189035], [3.596483291054488, 5.052226481991641], [2.9701097616320284, 5.531682243130713], [2.731096182040875, 4.745566004089044], [2.9982944633987256, 5.714256270854997], [3.1948091030692796, 6.070899492245685], [3.392895177777541, 4.2886647074932345], [4.332973785345426, 5.53678658538905], [3.1821907341696787, 5.6621112375151235]]
 )
 save({'qsvm_results': qsvm_results})
-
 ```

algorithms/qml/qsvm_pauli_feature_map/qsvm_pauli_feature_map.qmod

@@ -1,19 +1,18 @@
 qfunc main(output qbv: qbit[]) {
   allocate<2>(qbv);
   pauli_feature_map<QSVMFeatureMapPauli {
-    paulis = [
+    paulis=[
       [Pauli::Z],
       [Pauli::Z, Pauli::Z]
     ],
-    entanglement = QSVMFeatureMapEntanglement::CIRCULAR,
-    alpha = 2,
-    reps = 2,
-    feature_dimension = 2
+    entanglement=QSVMFeatureMapEntanglement::CIRCULAR,
+    alpha=2,
+    reps=2,
+    feature_dimension=2
   }>(qbv);
 }
 
 cscope ```
-
 qsvm_results = qsvm_full_run(
     train_data=[[2.0734511513692637, 5.843362335677016], [4.0212385965949355, 4.523893421169302], [1.9477874452256718, 1.6964600329384885], [3.9584067435231396, 2.8274333882308142], [4.272566008882119, 5.6548667764616285], [2.1362830044410597, 1.7592918860102844], [3.832743037379548, 0.3769911184307752], [2.450442269800039, 1.884955592153876], [5.969026041820608, 2.7646015351590183], [5.026548245743669, 1.3194689145077132], [4.775220833456486, 2.9530970943744057], [6.157521601035995, 1.6964600329384885], [4.1469023027385274, 1.6336281798666925], [5.215043804959057, 3.0159289474462017], [3.769911184307752, 3.0787608005179976], [3.769911184307752, 5.215043804959057], [2.324778563656447, 3.832743037379548], [5.089380098815465, 1.2566370614359172], [2.0734511513692637, 1.5079644737231008], [2.1362830044410597, 0.5026548245743669], [2.9530970943744057, 1.2566370614359172], [0.3769911184307752, 2.0734511513692637], [6.031857894892403, 5.717698629533424], [5.026548245743669, 0.7539822368615504], [4.900884539600078, 2.324778563656447], [1.6336281798666925, 6.031857894892403], [5.906194188748811, 4.39822971502571], [0.12566370614359174, 2.1362830044410597], [0.6911503837897546, 4.39822971502571], [3.0787608005179976, 4.586725274241099], [5.215043804959057, 4.71238898038469], [1.6964600329384885, 3.832743037379548], [0.25132741228718347, 1.9477874452256718], [3.204424506661589, 5.46637121724624], [4.71238898038469, 0.7539822368615504], [4.775220833456486, 0.8796459430051422], [3.1415926535897936, 2.6389378290154264], [5.026548245743669, 2.5761059759436304], [5.46637121724624, 6.157521601035995], [4.9637163926718735, 0.25132741228718347]],
     train_labels=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
@@ -22,5 +21,4 @@ qsvm_results = qsvm_full_run(
     predict_data=[[1.8221237390820801, 1.3194689145077132], [3.8955748904513436, 2.7646015351590183], [3.6442474781641603, 2.324778563656447], [0.942477796076938, 0.3141592653589793], [2.5761059759436304, 1.0053096491487339], [4.586725274241099, 2.324778563656447], [1.6964600329384885, 3.769911184307752], [3.0787608005179976, 1.7592918860102844], [6.031857894892403, 5.277875658030853], [5.906194188748811, 4.209734155810323]]
 )
 save({'qsvm_results': qsvm_results})
-
 ```