diff --git a/CHANGELOG.md b/CHANGELOG.md
index 81e106641..19d4211b2 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,16 +4,23 @@
 
 ### Added
 
-- Implement new method: `NystroemSketchInfluence` [PR #504](https://github.com/aai-institute/pyDVL/pull/504)
+- Implement new method: `NystroemSketchInfluence` 
+  [PR #504](https://github.com/aai-institute/pyDVL/pull/504)
 - Add property `model_dtype` to instances of type `TorchInfluenceFunctionModel`
+- Implement a preconditioned block variant of conjugate gradient 
+  [PR #507](https://github.com/aai-institute/pyDVL/pull/507)
 
 ### Fixed
 
-- Bug in `LissaInfluence`, when not using CPU device [PR #495](https://github.com/aai-institute/pyDVL/pull/495)
-- Memory issue with `CgInfluence` and `ArnoldiInfluence`[PR #498](https://github.com/aai-institute/pyDVL/pull/498)
-- Raising specific error message with install instruction, when trying to load `pydvl.utils.cache.memcached`
-  without `pymemcache` installed. If `pymemcache` is available, all symbols from
-  `pydvl.utils.cache.memcached` are available through `pydvl.utils.cache`[PR #509](https://github.com/aai-institute/pyDVL/pull/509)  
+- Bug in `LissaInfluence`, when not using CPU device 
+  [PR #495](https://github.com/aai-institute/pyDVL/pull/495)
+- Memory issue with `CgInfluence` and `ArnoldiInfluence`
+  [PR #498](https://github.com/aai-institute/pyDVL/pull/498)
+- Raising specific error message with install instruction, when trying to load 
+  `pydvl.utils.cache.memcached` without `pymemcache` installed. 
+  If `pymemcache` is available, all symbols from `pydvl.utils.cache.memcached` 
+  are available through `pydvl.utils.cache`
+  [PR #509](https://github.com/aai-institute/pyDVL/pull/509)  
 
 ### Miscellaneous
 
@@ -35,9 +42,10 @@
 ### Fixed
 
 - Bug in using `DaskInfluenceCalcualator` with `TorchnumpyConverter`
-  for single dimensional arrays [PR #485](https://github.com/aai-institute/pyDVL/pull/485)
-- Fix implementations of `to` methods of `TorchInfluenceFunctionModel` implementations
-  [PR #487](https://github.com/aai-institute/pyDVL/pull/487)
+  for single dimensional arrays 
+  [PR #485](https://github.com/aai-institute/pyDVL/pull/485)
+- Fix implementations of `to` methods of `TorchInfluenceFunctionModel` 
+  implementations [PR #487](https://github.com/aai-institute/pyDVL/pull/487)
 - Fixed bug with checking for converged values in semivalues
   [PR #341](https://github.com/appliedAI-Initiative/pyDVL/pull/341)
 
@@ -56,7 +64,8 @@
 - New influence function interface `InfluenceFunctionModel`
 - Data parallel computation with `DaskInfluenceCalculator`
   [PR #26](https://github.com/aai-institute/pyDVL/issues/26)
-- Sequential batch-wise computation and write to disk with `SequentialInfluenceCalculator` 
+- Sequential batch-wise computation and write to disk with 
+  `SequentialInfluenceCalculator` 
   [PR #377](https://github.com/aai-institute/pyDVL/issues/377)
 - Adapt notebooks to new influence abstractions
   [PR #430](https://github.com/aai-institute/pyDVL/issues/430)
diff --git a/docs/assets/pydvl.bib b/docs/assets/pydvl.bib
index c9583ce7d..ca2b3d85f 100644
--- a/docs/assets/pydvl.bib
+++ b/docs/assets/pydvl.bib
@@ -13,6 +13,23 @@ @article{agarwal_secondorder_2017
   langid = {english}
 }
 
+@article{bekas_estimator_2007,
+  title = {An Estimator for the Diagonal of a Matrix},
+  author = {Bekas, C. and Kokiopoulou, E. and Saad, Y.},
+  date = {2007-11-01},
+  journaltitle = {Applied Numerical Mathematics},
+  shortjournal = {Applied Numerical Mathematics},
+  series = {Numerical {{Algorithms}}, {{Parallelism}} and {{Applications}} (2)},
+  volume = {57},
+  number = {11},
+  pages = {1214--1229},
+  issn = {0168-9274},
+  doi = {10.1016/j.apnum.2007.01.003},
+  url = {https://www.sciencedirect.com/science/article/pii/S0168927407000244},
+  urldate = {2024-03-19},
+  abstract = {A number of applications require to compute an approximation of the diagonal of a matrix when this matrix is not explicitly available but matrix–vector products with it are easy to evaluate. In some cases, it is the trace of the matrix rather than the diagonal that is needed. This paper describes methods for estimating diagonals and traces of matrices in these situations. The goal is to obtain a good estimate of the diagonal by applying only a small number of matrix–vector products, using selected vectors. We begin by considering the use of random test vectors and then explore special vectors obtained from Hadamard matrices. The methods are tested in the context of computational materials science to estimate the diagonal of the density matrix which holds the charge densities. Numerical experiments indicate that the diagonal estimator may offer an alternative method that in some cases can greatly reduce computational costs in electronic structures calculations.}
+}
+
 @article{benmerzoug_re_2023,
   title = {[{{Re}}] {{If}} You like {{Shapley}}, Then You'll Love the Core},
   author = {Benmerzoug, Anes and Delgado, Miguel de Benito},
@@ -345,6 +362,19 @@ @inproceedings{schoch_csshapley_2022
   keywords = {notion}
 }
 
+@book{trefethen_numerical_1997,
+  title = {Numerical {{Linear Algebra}}},
+  author = {Trefethen, Lloyd N. and Bau, Iii, David},
+  date = {1997-01},
+  publisher = {{Society for Industrial and Applied Mathematics}},
+  location = {Philadelphia, PA},
+  doi = {10.1137/1.9780898719574},
+  url = {https://epubs.siam.org/doi/book/10.1137/1.9780898719574},
+  urldate = {2024-03-19},
+  isbn = {978-0-89871-361-9 978-0-89871-957-4},
+  langid = {english}
+}
+
 @inproceedings{wang_data_2022,
   title = {Data {{Banzhaf}}: {{A Robust Data Valuation Framework}} for {{Machine Learning}}},
   shorttitle = {Data {{Banzhaf}}},
diff --git a/docs/influence/influence_function_model.md b/docs/influence/influence_function_model.md
index 951ea8420..b265d705d 100644
--- a/docs/influence/influence_function_model.md
+++ b/docs/influence/influence_function_model.md
@@ -1,7 +1,9 @@
 In almost every practical application it is not possible to construct, even less
-invert the complete Hessian in memory. pyDVL offers several implementations of the interface
-[InfluenceFunctionModel][pydvl.influence.base_influence_function_model.InfluenceFunctionModel], which do not compute
-the full Hessian (in contrast to [DirectInfluence][pydvl.influence.torch.influence_function_model.DirectInfluence]).
+invert the complete Hessian in memory. pyDVL offers several implementations of 
+the interface [InfluenceFunctionModel
+][pydvl.influence.base_influence_function_model.InfluenceFunctionModel], 
+which do not compute the full Hessian (in contrast to [DirectInfluence
+][pydvl.influence.torch.influence_function_model.DirectInfluence]).
 
 
 ### Conjugate Gradient
@@ -11,26 +13,47 @@ method that does not require the explicit inversion of the Hessian. Instead, it
 only requires the calculation of Hessian-vector products, making it a good
 choice for large datasets or models with many parameters. It is nevertheless
 much slower to converge than the direct inversion method and not as accurate.
+
 More info on the theory of conjugate gradient can be found on
-[Wikipedia](https://en.wikipedia.org/wiki/Conjugate_gradient_method).
+[Wikipedia](https://en.wikipedia.org/wiki/Conjugate_gradient_method), or in
+text books such as [@trefethen_numerical_1997, Lecture 38].
+
+pyDVL also implements a stable block variant of the conjugate 
+gradient method, defined in [@ji_breakdownfree_2017], which solves several
+right hand sides simultaneously.
+
+Optionally, the user can provide a pre-conditioner to improve convergence, such 
+as a [Jacobi pre-conditioner
+][pydvl.influence.torch.pre_conditioner.JacobiPreConditioner], which
+is a simple [diagonal pre-conditioner](
+https://en.wikipedia.org/wiki/Preconditioner#Jacobi_(or_diagonal)_preconditioner) 
+based on Hutchinson's diagonal estimator [@bekas_estimator_2007],
+or a [Nyström approximation based pre-conditioner
+][pydvl.influence.torch.pre_conditioner.NystroemPreConditioner], 
+described in [@frangella_randomized_2023]. 
 
 ```python
 from pydvl.influence.torch import CgInfluence
+from pydvl.influence.torch.pre_conditioner import NystroemPreConditioner
 
 if_model = CgInfluence(
     model,
     loss,
     hessian_regularization=0.0,
-    x0=None,
     rtol=1e-7,
     atol=1e-7,
     maxiter=None,
+    use_block_cg=True,
+    pre_conditioner=NystroemPreConditioner(rank=10)
 )
+if_model.fit(train_loader)
 ```
 
-The additional optional parameters `x0`, `rtol`, `atol`, and `maxiter` are
-respectively the initial guess for the solution, the relative
-tolerance, the absolute tolerance, and the maximum number of iterations.
+The additional optional parameters `rtol`, `atol`, `maxiter`, `use_block_cg` and 
+`pre_conditioner` are respectively, the relative
+tolerance, the absolute tolerance, the maximum number of iterations, 
+a flag indicating whether to use block variant of cg and an optional
+pre-conditioner.
 
 
 ### Linear time Stochastic Second-Order Approximation (LiSSA)
@@ -62,6 +85,7 @@ if_model = LissaInfluence(
    h0=None,
    rtol=1e-4,
 )
+if_model.fit(train_loader)
 ```
 
 with the additional optional parameters `maxiter`, `dampen`, `scale`, `h0`, and
@@ -94,14 +118,26 @@ if_model = ArnoldiInfluence(
     rank_estimate=10,
     tol=1e-6,
 )
+if_model.fit(train_loader)
 ```
 
 ### Eigenvalue Corrected K-FAC
 
-K-FAC, short for Kronecker-Factored Approximate Curvature, is a method that approximates the Fisher information matrix [FIM](https://en.wikipedia.org/wiki/Fisher_information) of a model. It is possible to show that for classification models with appropriate loss functions the FIM is equal to the Hessian of the model’s loss over the dataset. In this restricted but nonetheless important context K-FAC offers an efficient way to approximate the Hessian and hence the influence scores. 
+K-FAC, short for Kronecker-Factored Approximate Curvature, is a method that 
+approximates the Fisher information matrix [FIM](https://en.wikipedia.org/wiki/Fisher_information) of a model. 
+It is possible to show that for classification models with appropriate loss 
+functions the FIM is equal to the Hessian of the model’s loss over the dataset. 
+In this restricted but nonetheless important context K-FAC offers an efficient 
+way to approximate the Hessian and hence the influence scores. 
 For more info and details refer to the original paper [@martens_optimizing_2015].
 
-The K-FAC method is implemented in the class [EkfacInfluence](pydvl/influence/torch/influence_function_model.py). The following code snippet shows how to use the K-FAC method to calculate the influence function of a model. Note that, in contrast to the other methods for influence function calculation, K-FAC does not require the loss function as an input. This is because the current implementation is only applicable to classification models with a cross entropy loss function. 
+The K-FAC method is implemented in the class [EkfacInfluence
+][pydvl.influence.torch.influence_function_model.EkfacInfluence]. 
+The following code snippet shows how to use the K-FAC method to calculate the 
+influence function of a model. Note that, in contrast to the other methods for 
+influence function calculation, K-FAC does not require the loss function as an 
+input. This is because the current implementation is only applicable to 
+classification models with a cross entropy loss function. 
 
 ```python
 from pydvl.influence.torch import EkfacInfluence
@@ -109,10 +145,23 @@ if_model = EkfacInfluence(
     model,
     hessian_regularization=0.0,
 )
+if_model.fit(train_loader)
 ```
-Upon initialization, the K-FAC method will parse the model and extract which layers require grad and which do not. Then it will only calculate the influence scores for the layers that require grad. The current implementation of the K-FAC method is only available for linear layers, and therefore if the model contains non-linear layers that require gradient the K-FAC method will raise a NotImplementedLayerRepresentationException.
-
-A further improvement of the K-FAC method is the Eigenvalue Corrected K-FAC (EKFAC) method [@george_fast_2018], which allows to further re-fit the eigenvalues of the Hessian, thus providing a more accurate approximation. On top of the K-FAC method, the EKFAC method is implemented by setting `update_diagonal=True` when initialising [EkfacInfluence](pydvl/influence/torch/influence_function_model.py). The following code snippet shows how to use the EKFAC method to calculate the influence function of a model. 
+Upon initialization, the K-FAC method will parse the model and extract which 
+layers require grad and which do not. Then it will only calculate the influence 
+scores for the layers that require grad. The current implementation of the 
+K-FAC method is only available for linear layers, and therefore if the model 
+contains non-linear layers that require gradient the K-FAC method will raise a 
+NotImplementedLayerRepresentationException.
+
+A further improvement of the K-FAC method is the Eigenvalue Corrected 
+K-FAC (EKFAC) method [@george_fast_2018], which allows to further re-fit the 
+eigenvalues of the Hessian, thus providing a more accurate approximation. 
+On top of the K-FAC method, the EKFAC method is implemented by setting 
+`update_diagonal=True` when initialising [EkfacInfluence
+][pydvl.influence.torch.influence_function_model.EkfacInfluence]. 
+The following code snippet shows how to use the EKFAC method to calculate the 
+influence function of a model. 
 
 ```python
 from pydvl.influence.torch import EkfacInfluence
@@ -129,10 +178,12 @@ if_model.fit(train_loader)
 This approximation is based on a Nyström low-rank approximation of the form
 
 \begin{align*}
-H_{\text{nys}} &= (H\Omega)(\Omega^TH\Omega)^{+}(H\Omega)^T \\\
-&= U \Lambda U^T
+H_{\text{nys}} &= (H\Omega)(\Omega^TH\Omega)^{\dagger}(H\Omega)^T \\\
+&= U \Lambda U^T,
 \end{align*}
 
+where $(\cdot)^{\dagger}$ denotes the [Moore-Penrose inverse](
+https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse),
 in combination with the [Sherman–Morrison–Woodbury formula](
 https://en.wikipedia.org/wiki/Woodbury_matrix_identity) to calculate the
 action of its inverse:
@@ -142,8 +193,8 @@ action of its inverse:
 \frac{1}{\lambda}(I−UU^T)x,
 \end{equation*}
 
-see also [@hataya_nystrom_2023] and [@frangella_randomized_2021]. The essential parameter is the rank of the
-approximation.
+see also [@hataya_nystrom_2023] and [@frangella_randomized_2023]. The essential 
+parameter is the rank of the approximation.
 
 ```python
 from pydvl.influence.torch import NystroemSketchInfluence
@@ -156,6 +207,7 @@ if_model = NystroemSketchInfluence(
 if_model.fit(train_loader)
 ```
 
-These implementations represent the calculation logic on in memory tensors. To scale up to large collection
-of data, we map these influence function models over these collections. For a detailed discussion see the
+These implementations represent the calculation logic on in memory tensors. 
+To scale up to large collection of data, we map these influence function models 
+over these collections. For a detailed discussion see the
 documentation page [Scaling Computation](scaling_computation.md).
diff --git a/notebooks/influence_wine.ipynb b/notebooks/influence_wine.ipynb
index da521f4b5..8e226f047 100644
--- a/notebooks/influence_wine.ipynb
+++ b/notebooks/influence_wine.ipynb
@@ -279,7 +279,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0f86315dfd2143159376ad740f4b841e",
+       "model_id": "dc79c95e3db747dc8e8fee32dde79f22",
        "version_major": 2,
        "version_minor": 0
       },
@@ -341,7 +341,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -519,7 +519,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -566,8 +566,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average influence of corrupted points:  -1.0704405\n",
-      "Average influence of other points:  0.10983819\n"
+      "Average influence of corrupted points:  -1.079615\n",
+      "Average influence of other points:  0.109443866\n"
      ]
     }
    ],
@@ -633,7 +633,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -713,17 +713,13 @@
   {
    "cell_type": "markdown",
    "id": "5bc615e637c1ee40",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "metadata": {},
    "source": [
     "Since the Hessian is symmetric and positive definite (at least after applying a sufficient regularization), we can utilize the [Conjugate Gradients Algorithm](https://en.wikipedia.org/wiki/Conjugate_gradient_method) to approximately solve the equations\n",
     "\n",
     "$$ (H + \\lambda \\operatorname{I}) x = b$$\n",
     "\n",
-    "Most importantly, the algorithm do not require the computation of the full Hessian matrix, but only requires the implementation of Hessian vector products.\n"
+    "Most importantly, the algorithm do not require the computation of the full Hessian matrix, but only requires the implementation of Hessian vector products. pyDVL implements a stable block variant of preconditioned conjugate gradients algorithm.\n"
    ]
   },
   {
@@ -739,28 +735,17 @@
      "hide-output"
     ]
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "db8b09eab0b845a6b30513b740fff70c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Conjugate gradient:   0%|          | 0/54 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "from pydvl.influence.torch.pre_conditioner import NystroemPreConditioner\n",
+    "\n",
     "cg_influence_model = CgInfluence(\n",
     "    nn_model,\n",
     "    F.cross_entropy,\n",
     "    hessian_regularization=0.1,\n",
     "    progress=True,\n",
+    "    use_block_cg=True,\n",
+    "    pre_conditioner=NystroemPreConditioner(rank=5),\n",
     ")\n",
     "cg_influence_model = cg_influence_model.fit(training_data_loader)\n",
     "cg_train_influences = cg_influence_model.influences(\n",
@@ -801,7 +786,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage error of Cg over direct method:25.564396381378174 %\n"
+      "Percentage error of Cg over direct method:22.570940852165222 %\n"
      ]
     }
    ],
@@ -827,7 +812,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -871,8 +856,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation Cg vs direct 0.9977465941297974\n",
-      "Spearman Correlation Cg vs direct 0.9955664155981561\n"
+      "Pearson Correlation Cg vs direct 0.9977863783358302\n",
+      "Spearman Correlation Cg vs direct 0.9956671788800161\n"
      ]
     }
    ],
@@ -890,11 +875,7 @@
   {
    "cell_type": "markdown",
    "id": "c273f980987b1057",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "metadata": {},
    "source": [
     "### Lissa"
    ]
@@ -902,11 +883,7 @@
   {
    "cell_type": "markdown",
    "id": "fcb66102c654f5aa",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "metadata": {},
    "source": [
     "The LiSSA method is a stochastic approximation of the inverse Hessian vector product. Compared to conjugate gradient it is faster but less accurate and typically suffers from instability.\n",
     "\n",
@@ -923,9 +900,6 @@
    "id": "ddf5d245e72bd4c2",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -937,7 +911,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bb5f5439d2794135a183a9c97872c6f1",
+       "model_id": "6190a894ffb0420fad95b73afd230a2a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -969,9 +943,6 @@
    "id": "fd63d3946653ecb5",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -984,7 +955,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage error of Lissa over direct method:9.107763320207596 %\n"
+      "Percentage error of Lissa over direct method:15.77848345041275 %\n"
      ]
     }
    ],
@@ -1000,9 +971,6 @@
    "id": "c0f6168f3163ccda",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1013,7 +981,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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P3+4XcLllukWc/iDJc9L9E2dqga2/SNdr+T6ttdMGr1VVz0oXds7HG9MFgk9IsindsOd1+dmw6sHPZa41zmbq+bO75+V8zPSeMHWdp7fW/n4O57iwv59P7++lNvU5/mdr7eFLXMN83je2pXuf22OGwHOY7z8AE8acnQAwHhckObhf6Xa6O82w7VP9/dHjK+nqa/zKPNqOop6pIZ3HTN9RVTdON6zyeyMYDjw1pHM+vSYH3bi/f8cM++Y7LHapfD7d73f3mGHfLw55zl/oh6hPd8zANdMPm/1OksOr6iYztL9Pf/+5IevYnR2Zw/e+tfbT1tpHWmt/kuRF6f4pMZfXxJKba+2t85XW2ivzs16JDx1ocuN0PeNOm+EywzzXT0kXtD+unwfyCel68F1jbuB51DibqefPNeqsqg0ZmJNygeb1Htha+2m6xZ0Orqq5TNWw0PerUfh6upD2brP8rJrJqOv+Rrp5iW/XTxcx3UzvG59L9z4303vaMSOqC4AVTNgJAONxRrqeTU8a3FhVT0xyzxnavyHdH53PraprLLxSVWuq6pgF1vSedItaPLjvqTj9GoNzub0rXXD1B1X1qzOdrKruXlV7z+G6r+/v/6Kqpua5S1WtTfLSdL+PvG5On8Gu/bi/v/6Qx2/p748Z3FhVv5zFXUBpId7U37+wX/gjydUh0O56zM1mQ7ped1erqjul6723Lcl/Dux6fboekC/pv79T7Q8cuP7rMx4/TnK9qpo+z2qq6l5VNdOIpqnegZeMqaYFm2vtVXWrqpqpt+NMn+OWdD3jbjvtWr+dnw0tn7PW2qVJ/j1dr8Y/TnK7JO/tF6YaPP98apzNu9L9M+mx/fNw0PPys2HRC9IvfnR6kodX1W/N1KaqbtMP2Z8y1QP01f1rbrDtmqo6dGDTQt+vFqzv/f3KdL0n/36W186h0xbGuyBdT9iR1N0vpPbmJPtm2uJCVXWjJH+U5Mok/zqwa2qRsr+uqr0G2l83Xa9lAFY5w9gBYDxemS7o/Kequl+6RVVun27Ri/9KNwz1aq21H1fVcemCo0/1c+B9Jd0flUf2xx2Qay4iMmettSuq6pHp5qJ8S1U9JV3vpb3SLUBxv/S/G/QLSTw8yQfSDZ/9RJIvpAsjjky3UvMN0/2RvMuAorX2iap6cZL/l+T/qurkJD9N1yPt1ukWmXjJsJ/XgE/2tRxfVQfkZ3O3vXKOCx+9Kt337D/6Gn/Y1/fAdEHOr4+gxnF7U5JHp6v5/6rq3enm6HtEkv9NtxjIztkPn9HHkvxOVd013WIyh6b7WqxJt+r04OJJL033fX1Iki9W1XvTLT7yyCQHpVv1etiFknbnw+mel++vqo8luTzJF1tr70kXQh1eVf+TLui7Iskd0w2h/n6St814xtF6aFVtnGXfB1trs/WCnGvtD0gXMn8yyTeT/Chdr+mHpPueD77GXpEu1Px4Vf17utD6Tul6yp2c5Lj5f3p5Y7p/CvzNwOPp5lPjjFprF1fVk5O8Pcnp/aJNZ/e13zrd8/VeQ9Q/k8emm9v0dVX1R0k+ne6fUkckuW1/vbv3n0eS/Eu6nqC/meRbVfWuJOclOSzd9+v16QLZZOHvV6PyV+nC6d9L8qCq+ki6eUcPSjeX5z2T/Hn6hfH6r/+nkxxdVW9O933ckeTdC5iCZVO6r9sfVtWd0y3CdWCSR6ULQf+wtfa9gfZvTfce9OB073PvSvc+d1y697kbDVkHABNC2AkAY9Ba+2pV3T/dUNMHpRvSeXq6P4wfnmlhZ3/Mh/ueVs9IF0QcnS7Y+GG6P7hnGl4937o+U1W3T/fH5a+kG+58UZJvZ1rvvdbal6rqdunmsvy1dEHgznTBwufTLWhx/hyv+8yq+nySP0zy+HR/mH4nXS+cv+t79yz0c7ugqh7R1/XEJNfud/1b5jBPZP/53ifJC5Mcm+73pC+m+35dmBUQdrbWWlU9LMmz0wUuT0v3/XpjujD3oemGG8/H99IFISf09+vTDSN9QWvtA9Ouf0VVPSDdc+ax/fWvSvd1PL619tbhPrM5eWG6+XAflC6gWZvu835Putfhw9IFevdP9zz+Qb/9Fa21C8ZY15Tb9beZXJjZh3zPtfYPpOttd6904eF+6b73/53kZa21T0ydsLX2/qp6ULrX36+nC6vOSDdk+IYZIuxsrX28qr6dfoh8un/qTDfnGndzrZOr6oHpXuuPShdsfyzd++umjCjs7BdfumO65/Ej0vVmXpsumPxqun9qfXmgfUvy+Kr6QJIn97Wt7z/H09MtBDXVdkHvV6PS/2ProUke19fxa+kWJDov3Wv/L9P1vBz0m0lenu6fKo9J15t7a5Khws7W2k+q6u5JnpXu/fZP0q0Cf0aSl7TWPjitfev/cbepr/kP032N35DkBUkuG6YOACZHdT+TAQCYZH0I+cEkJ7TWnjWH9hvThR1vbK09cbzVAQDAaJizEwBgglTVYTNsOyBdz8zk5+fYBACAiWIYOwDAZHlZP/3AJ9INRT0i3ZQF103y6tbaGUtZHAAAjJOwEwBgspySbnXrB6Wbw/KydItdvS6jWfUeAACWLXN2AgAAAAATwZydAAAAAMBEEHYCAAAAABPBnJ3zVFWV5LAkFy11LQAAAACwQu2b5IdtxHNsCjvn77AkW5e6CAAAAABY4Y5IctYoTyjsnL+pHp1HRO9OAAAAAJivfdN1Jhx5tibsHN5FrbXtS10EAAAAAKwk3SyR42GBIgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjrlroAAAAAAFjpNm7avDbJ0UkOTXJ2ktO3nHDsjqU6z2qlZycAAAAALMDGTZsfnmRLklOTvKW/39JvX/TzrGbCTgAAAAAYUh9Enpzk8Gm7Dk9y8lyDylGdZ7Wr1tpS17CiVNV+SbYl2dBa277U9QAAAACwNPoh51vSBZI1Q5OWZGuSo3Y1FH1U51kpxpmv6dkJAAAAAMM5OskRmTmgTL/9yL7dYpxn1RN2AgAAAMBwDh1Ru1GdZ9UTdgIAAADAcM4eUbtRnWfVE3YCAAAAwHBOTzeX5myL4rQkZ/btFuM8q56wEwAAAACG0C8W9PT+4fSgcurx8btbVGhU50HYCQAAAABD23LCsackOS7JWdN2bU1yXL9/0c6z2lVrs/WOZSZVtV+SbUk2tNa2L3U9AAAAACy9jZs2r023Wvqh6ebWPH2YnpijOs9yNs58Tdg5T8JOAAAAABjeOPM1w9gBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJsK6pS4AAAAAgMWzcdPmtUmOTnJokrOTnL7lhGN3LG1VMBp6dgIAAACsEhs3bX54ki1JTk3ylv5+S78dVjxhJwAAAMAq0AeaJyc5fNquw5OcLPBkEgg7AQAAACZcP3T9xP5hTds99fgVfTtYsYSdAAAAAJPv6CRH5JpB55RKcmTfDlYsYScAAADA5Dt0xO1gWRJ2AgAAAEy+s0fcDpYlYScAAADA5Ds9ydYkbZb9LcmZfTtYsYSdAAAAABNuywnH7kjy9P7h9MBz6vHxfTtYsYSdAAAAAKvAlhOOPSXJcUnOmrZra5Lj+v2wolVrs/VeZiZVtV+SbUk2tNa2L3U9AAAAAPOxcdPmtelWXT803Rydp+vRyWIaZ74m7JwnYScAAAAADG+c+Zph7AAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARFjxYWdV/UFVbamqy6rq01V1l120fWJVtWm3yxazXgAAAABgPFZ02FlVv57kZUmen+QXknwxyQeq6qBdHLY9yaEDtxuMu04AAAAAYPxWdNiZ5E+SvLa19obW2leT/F6SS5L81i6Oaa21cwZu5y5KpQAAAADAWK3YsLOq9kxyxyQfmtrWWtvZP777Lg7dp6q+X1VnVtW7qupWu7nO+qrab+qWZN9R1A8AAAAAjNaKDTuTHJhkbZLpPTPPTXLILMd8I12vz4ckeVy6z/8TVXXELq7zrCTbBm5bF1AzAAAAADAmKznsnLfW2idba29qrX2htfbRJA9Pcl6Sp+zisL9JsmHgtqtgFAAAAABYIuuWuoAFOD/JjiQHT9t+cJJz5nKC1tqVVfX5JDfeRZvLk1w+9biq5l8pAAAAADB2K7ZnZ2vtiiSfTXK/qW1VtaZ//Mm5nKOq1ia5TZKzx1EjAAAAALB4VnLPziR5WZI3VtVnkpyR5Pgk107yhiSpqjclOau19qz+8XOSfCrJt5Psn+TPktwgyb8sduEAAAAAwGit6LCztfb2qrpekhekW5ToC0ke2FqbWrTo+kl2DhxynSSv7dtekK5n6D1aa19dtKIBAAAAgLGo1tpS17CiVNV+6VZl39Ba277U9QAAAADASjLOfG3FztkJAAAAADBI2AkAAAAATARhJwAAAAAwEYSdAAAAAMBEEHYCAAAAABNB2AkAAAAATIR1S10AAAAAsDJt3LR5bZKjkxya5Owkp2854dgdS1sVsJrp2QkAAADM28ZNmx+eZEuSU5O8pb/f0m8HWBLVWlvqGlaUqtovybYkG1pr25e6HgAAABiX2Xpu9oHmyX2zGjhkKmQ4bssJx56yeJUCK8k48zVh5zwJOwEAAFgN+kDzxCRHDGzemuSPk7w8yeH5+aBzSuvbHWVIOzCTceZrhrEDAAAAP2eg5+bh03YdnuTf0wWgMwWd6bcfma5HKMCiskARAAAArGIzDFX/n3Q9OpNrBpqVnw1V351DR1IgwDwIOwEAAGCVmmWo+nlJrreLw2br0Tnd2cPWBTAsYScAAACsQtMWGRp04BxP0bLrOTtPH7I0gKGZsxMAAABWmX7o+q6Gqs/FTEPapx4fb3EiYCkIOwEAAGD1OTq7XmRoV1qSM5M8MslZ0/ZtTXLclhOOPWVh5QEMxzB2AAAAmGAzLEB0eua+eND0oeqDPTdP2bhp839OP7cencBSqtbmuogaSVJV+yXZlmRDa237UtcDAAAAs5llAaKtSV6T5AVzOMX0xYrOTB90jqxIYNUZZ74m7JwnYScAAAArwbQFiGbqnfmTJNfNrhcZulGSe0bPTWCExpmvGcYOAAAAE2YOCxC1/Cz03NVQ9SuTnDamMgFGzgJFAAAAMHl2twBRJTkwyXNjkSFggujZCQAAAJNnrgsQfTvJxlhkCJgQwk4AAACYPGfPtV0fbJ42xloAFo1h7AAAADB5Tk83HH22VYlbupXVT1+0igAWgdXY58lq7AAAACymfrGheQ8zn8Nq7OblBJbEOPM1PTsBAABgmeoDyy1JTk3ylv5+S799l/og87hYgAhYRfTsnCc9OwEAABi3vjfns5M8v980dM/MYXuGAozLOPM1Yec8CTsBAAAYp77X5olJjthFs5auh+ZRgktgpTGMHQAAAFaBgXk2dxV0Jl1PzyPT9dgEoCfsBAAAgGWgH25+4jwPO3QctQCsVOuWugAAAAAgSddLc3c9Oqc7exyFAKxUwk4AAABYBHNYKGg+vTSn5uw8fXQVAqx8hrEDAADAmPVzcW5JcmqSt/T3W/rtU+baS3NqpeHjLU4E8POEnQAAADBGA4sOHT5t1+FJTh4IPE9P11uzZde2JjluywnHnjLSQgEmgLATAAAAxmTaokM1bffU41ds3LR5bd9L8+n9tumB59Tj5yQ5StAJMDNhJwAAAIzP1KJD04POKZXkyL5d+hDzuCRnTWu3Nckjtpxw7F8Zug4wO2EnAAAAjM9cFx26ul0feG5Mcp8kj+3v9eYEmAOrsQMAAMD4zHXRoZ9r1/fePG3k1QBMOD07AQAAYHx2t+hQS3Jm3w6ABRJ2AgAAwJjMcdGh483DCTAawk4AAAAYo90sOnScuTgBRqdam60nPTOpqv2SbEuyobW2fanrAQAAYGXYuGnz2nSrrh+abo7O0/XoBFajceZrws55EnYCAAAAwPDGma8Zxg4AAAAATIR1S10AAAAALKaFDCc3FB1geTOMfZ4MYwcAAFi5Nm7a/PAkJyY5YmDzeUn+Lcm7s4vwcpZjtyZ5ukWGAObOnJ3LiLATAABgZerDypP7hzVLsxnDy10cO/VHtVXVAeZI2LmMCDsBAABWnn74+ZYkh2f2oDOZIbycw7EtXUh6lCHtALtngSIAAABYmKPTDT/fVdCZgf2v6EPOuRxbSY7s2wGwhISdAAAArAaHzaPt9PDy0DkeN9d2AIyJsBMAAICJ1s+3+YohDp0KL8+eY/u5tgNgTNYtdQEAAAAwDv0w9Gcnef6Qpzi3vz893Zycu5uz8/QhrwPAiOjZCQAAwMTpe3NuSfKCdAHl7ubqnFW/6NDT+4fTV/mdeny8xYkAlp6wEwAAgInSB50np1tUaCEOnvqgX5n9uCRnTWuzNQMrtwOwtAxjBwAAYGL0Q9dPHNHpzh4459FJ1id5Qr/v4H7/6Xp0Aiwfwk4AAAAmydFZeI/Oq+fg7HuJnjjtnFuTPH3LCceetsDrADBihrEDAAAwSQ7dfZNdunoOziQPSTcc/vBpbQ5PcnIfhAKwjOjZCQAAwIoxMKT80Mw8jPzsBV5ia7qg813pFjhKrrm4UaULRV+xcdPmdxnGDrB86NkJAADAijCwwvqpSd7S32+Z1sPy9CQ/meMpW5Izk9wvyWOT3CfJUf1iQ1PD4Wdbxb2SHNm3A2CZ0LMTAACAZW9ghfXp4eMR6YaUH7flhGNP2XLCsTs2btr8iiQvmOOpj99ywrEfmWH7XIfDL3TYPAAjpGcnAAAAy1o/dP012XUvy1f37ZLkRUl+vJvT7kjyqL4X50zmOhx+ocPmARghYScAAADL3b2THLCbNgf27dLPofnkdMPU27R2U48fveWEY0/exflOTzd/5/TjB89zZt8OgGVC2AkAAMCys3HT5rUbN20+ZuOmzY9J8oQ5HnbM1Ad9j83jkpw1rc3WJI/YTdA5FZg+vX84W2B6vMWJAJYXYScAAADLygwLET1+mPP0gefGdAsPTV+AaK7HzxaYHjfX8wCweKq12XrkM5Oq2i/JtiQbWmvbl7oeAACASTKwEFEy+xyds7nfLIsNLUg/F+jR6RYjOjvJ6Xp0AgxvnPmasHOehJ0AAADj0YeKW5IcnvkHnecnOUQICbD8jTNfWzfKkwEAAMACHJ3kiCGPfYqgEwBhJwAAACOxq+HecxwKfugQlz0z3UJB5s8EQNgJAADAwvVzbZ6Yn++ZuXXjps1TK5rPuG9aSHn2HC/3x0nOjfkzAZjGnJ3zZM5OAACAn7eLRYXawLY2y76rVzWfw5ydLd1K6EcJOAFWrnHma2tGeTIAAABWlz6gPLF/OD2grIFtM+1Lklf050gfYE71BJ3eM2fq8fGCTgBmI+wEAABgIaYWFZrv6unpjzmyP0eSpO/leVySs6a13ZqBXqAAMBNhJwAAAAsxzKJCuzxHH2huTHKfJI/t748SdAKwOxYoAgAAYCFuPIJzXGNhon6o+mkjODcAq4ienQAAAAxl46bNeyR5xgJPsyPJ/4ygHAAQdgIAADB//QrsZyXZb4GnWpvknguvCAAMYwcAAFi1+lXQj05yWJKDkvwoyQ+TnD644nnfg/MPktwoyXfShZxvz3CLEs1kFPN+AoCwEwAAYDXqe2aemG4l9em2bty0+TVJvpfkj5PcIaMLNmdyjTk7AWAYwk4AAIBVpg86T87sAeYRSV6wCKW0JFuTnL4I1wJgFTBnJwAAwCrSD10/cREveUW6ULNN2z71+PjBIfMAsBDCTgAAgNXl6HQ9N8c5LH3QY5Icl26ez0Fbkxy35YRjT1mkOgBYBQxjBwAAWF0WazGgM9P12jwlSTZu2vyudEHroenm6Dxdj04ARk3YCQAAsLrceIznbul6jD4nyYsGw8z+49PGeG0AEHYCAACsFv3CRM8f4yW2ZqA3JwAsNmEnAADAKrBx0+Y9kvzziE/7kiTvjaHpACwTwk4AAIAJ1oecr07y2CTrR3TaluQlW0449pkjOh8AjISwEwAAYEJt3LT5b5M8I8maEZ/6l7accOyHRnxOAFgwYScAAMAE6oPO/zfi07Z083KeOuLzAsBICDsBAAAmwMZNm9cmOTrJYenm0HzGAk85tbL64OOkW4DIvJwALEvCTgAAgBVu46bNxyV5VZLrjfC05087n5XWAVj2hJ0AAAAr2BiGq08NVb9RknvGSusArCDCTgAAgBWmH7J+7yS/n+S4EZ56cKj6lUlOG+G5AWDshJ0AAAArRB9yPjvJnyXZdwyXMFQdgBVN2AkAALCMDSw89OAkv51kvxGe/iVJ3htD1QGYEMJOAACAZWrjps2/nuRfkuwz4lP/NMkTt5xw7MkjPi8ALClhJwAAwDK0cdPmTyW56whPeWmSk5O8KcmpenACMImEnQAAAMvAxk2b90jyh0l+Mcmdkxw5olNPLTr0OHNxAjDphJ0AAABLbOOmzX+b5BlJ1ozh9BYdAmDVEHYCAAAsoT7o/H8jPu0FSd6Y5F2x6BAAq4iwEwAAYIls3LR5fboenaPSkjw3yYsEnACsRsJOAACARbJx0+a1Se6d5JgkN0/yKxnd0PXzkzzFcHUAVrORhJ1VtSHJxa01/zkEAACYpg85n51uuPo+Y7jEc5P8td6cAKx2Q4edVXWnJC9Mcq8keyb5pSQfqaoDk7wuyctba6eNokgAAICVauOmzQ9P8pokB4zh9HpzAsCAoYZLVNU9knw8yU2S/NvgeVpr5yfZkOQpoygQAABgpeqDzpMz+qDzx0mek+QQQScA/MywPTtflORrSe6WZN8kvzNt/6lJnrCAugAAAFa0fuj6a5LUCE53ZZLNSd6RZGussA4AMxo27Lxzkme11i6vqpnmmzkrySHDlwUAALDi3Tuj69H5aD04AWD3hg07r8yuh8AfnuTiIc89L1X1B0n+LF24+sUkT2utnbGL9o9M8ldJNib5VpJnttbeuwilAgAAE2Tjps3rk7wsyX3S/Y30r0lO3HLCsVf2TY4ZwWV+nOTJgk4AmJuh5uxM8qkkx820o6quneRJST46bFFzVVW/nu6Xi+cn+YV0YecHquqgWdrfI8lb0y2gdIck70zyzqq69bhrBQAAJsPGTZvXbty0+eNJLkvy1CS3SHLbJC9JcvnGTZv/dgGnb/3tP5LcL8nBgk4AmLtqrc3/oKq7pgszP5wuPHxTkj9NclGSZyQ5MsndW2tfGl2pM9bx6ST/21r7w/7xmiRnJnlla+2EGdq/Pcm1W2u/NrDtU0m+0Fr7vTlec78k25JsaK1tH8GnAQAArAD9HJx/2d9213HkxUk+kO5vpvk4M8nxAk4AJtk487Whws6+qPsm+ad0K7IP+k6S32mtjbVnZ1XtmeSSJMe11t45sP2NSfZvrT1khmN+kORlrbVXDGx7fpKHttZuN8t11idZP7Bp33QTggs7AQBgFehDzr9IF3KuneNhO5JcO916Brubt3NbktcneXcsPATAKjDOsHPec3ZWVaUL/D7RWrtZVd0+XeC5Jl3Q+dk2bII6Pwem+0Xj3Gnbz01y81mOOWSW9rtaTOlZSZ47TIEAAMDKtnHT5ocn+bck15rnoWuT/H6SJ6dbQX02z03y1wJOABiNYebs3DPJT5L8UZK01r7QWvuP1trbW2ufWaSgczH9TZINA7cjlrYcAABgMWzctPm4dEHlfIPOKTfuh6M/It3osEFnJnnElhOOfYGgEwBGZ949O1trl1fVOUkuH0M983F+uqEhB0/bfnCSc2Y55px5tk9r7fIMfK5dx1YAAGAS9UPWj07ykPQdPBbg8CTZcsKxp2zctPld/XkPTXJ2DFcHgLGYd9jZOynJ46vqn1prV4ywnjlrrV1RVZ9Nt0LhO5OrFyi6X5J/mOWwT/b7XzGw7QH9dgAAYBXrh6yfmNGN5jpr6oM+2DxtROcFAGYxbNj55SQPTfKVqjopyZYkl05v1Fob9wqCL0vyxqr6TJIzkhyfbhLwNyRJVb0pyVmttWf17U9M8tGq+tMkm5M8Osmd0s2jAwAArFJ90HnyiE/77RGfDwDYjWHDzrcOfPxXs7RpmftKhUNprb29qq6X5AXpFhn6QpIHttamFiG6fpKdA+0/UVWPTfLCJC9K8q10K7H/3zjrBAAAlpeB4eqHplu09MR+16jmrdqR5B9HdC4AYI5qmPWEqurec2nXWvvovE++zFXVfkm2JdnQWtu+1PUAAADzM4bh6jN58ZYTjn3mGM8PACvWOPO1oXp2TmKICQAATL4+6HzHGC+xI8nfCToBYGkMO4z9alV1yyQ36B9+v7X21YWeEwAAYJT6Yev3TvLGEZ96a5L/TvLTJN9J8o9bTjj2yhFfAwCYo6HDzqp6SLoFgjZO2/69JH/SWnv3wkoDAABYuDEMW39/kjenCzpP71daBwCWgaHCzqr61XRDP76f5NlJvtbvukW6lc1Pqapfa629fyRVAgAADGHEq6yfmeT4LScce8qIzgcAjNiwCxR9Msn6JEe31n46bd+1k3w8yWWttbuPpMplxAJFAACwMvRD17ckOTwLW2X9oiQvSfIivTgBYOGW3QJFSW6b5NnTg84kaa39tKpOSvKihRQGAAAwFwPzcR7TbzotyUeTHJ3hhq63/Hw4uk+S5yf5ShK9OgFgGVsz5HGXJbnuLvZft28DAAAwNv0w9XOTfDjJX/a3D/fbHjzkaaf3Ap16/Io+WAUAlqlhw86PJHl6VV1jmHpV3TXJHyX50EIKAwAA2JWB+TgPmGH3AUn+eISXqyRHpustCgAsU8MOY/9/ST6Z5ONVdUaSb/Tbb5bkLkl+lOSZCy8PAADgmvoelidm93Nx7kjXyWN37X6cmUPT6Q6dQxsAYIkM1bOztfa9dPN2/n2S6yT59f52nXS/cNyutbZlRDUCAABMN9f5OKeGnU9fmbX1t5cnuU+SR83xumfPsR0AsASG7dmZ1tqP0g0LGeXQEAAAgLmYTw/LVyR5ZH4+HN2a5PgtJxx7SnJ1T9GtmX3l9tbvP32YYgGAxTFUz86qWtcvET/b/v2qauggFQAAYDfm08Py3Uk2puvB+dj+/qipoDNJtpxw7I4kT+8fztQLNOnC0R1DVQsALIpqbfrP8TkcVPWqJPdqrd16lv1fTvKR1trTZ9q/kvUh77YkG1pr25e6HgAAWI36nphbsvuh7GemCzbnFFL2ix6dOO28Z2agFygAsDDjzNeGXY39gelWPZzNyUl+dchzAwAA7NJAT8zd9d6YV2/MPtDcmF30AgUAlq9he3ZeluRprbXXzrL/d5Oc2Frbe4H1LTt6dgIAwBhVrb9yzdrjtu21z/F77rjqsGo7L937yss2r23tVWntG9Ob9z0xX5NrrqR+fpKnCCkBYPkZZ742bNh5VpK3ttaeMcv+lyV5bGvtkAXWt+wIOwEAYEyqbnPJHus/vPeVl1/vk9e/Tb5y0A1z7SsuzQO/+cl2ncsuqnTDy/80rf1cT81+SPu9kxzTbzotyUfNrwkAy9NyDDtfl+RR6ebt/Py0fb+Q5GNJ/qO19qSRVLmMCDsBAGBh+nDy6HQrqp+d5PQtf/trR16+do8vfueAI/b7owf9Wb594PWvbr/nVVfmNz+/OX/+kde1NWmvSGt/skSlAwAjsBzDzsOS/G+Sg9KtbPiVftetkzwoyY+S3LW1tnVEdS4bwk4AABjeLAsAbf3g6576rf0vvehev/xb/7D2gr03zHjs7376lPz5aa9PkpuktW+Pv1oAYByW3QJFrbUfJrlTkrckuV+Sv+hv903y5iR3nsSgEwAAGF4fdJ6c5PDB7Rsuvejw6194zn1OuuODZg06k+RNv3BsLtxrn7aj6vfHXCoAsEINuxp7Wmtnt9aekOQ6SQ7pb9dprT2xD0MBAACSXD10/cT+YQ3uu8053669rroim2/+i7s8x+V7rM+HbnzXunjPvX9lTGUCACvc0GHnlNb5UbrVDq9XVbW7YwAAgFXn6HRD16/x98KeO65Mklyyx167Pckle+yVnbVm71EXBwBMhjmHnVV106p6fFVdZ9r2DVX1piSXpJtc/Lyq+sMR1wkAAKxsh86246wNByVJbnPO7qfhvM05384Va/cwZRYAMKN182j7p0kemORfp21/dbqV2b+V5EtJ7pHkxKra2lp75yiKBAAAVo6ZVlvv72f0jQNvkC8dcuM8/nOb85Eb32XW8976nG+1O5z9jbpizbqXjbpmAGAyzGcY+z2T/FcbWL69qo5MF3R+MsmtWmuPTHKrJN9N8gejLBQAAFj++kWItiQ5Nd2Cpqf2jw9MsjVJu8ZBVXntnR/WjvneZ/M373/lR2c670EX/6Sd+J6X1kV7XuuHe+686t1jKh8AWOHmE3YenuTr07b9WrpfVk5srV2VJK21C5O8KckdRlEgAACwMsy22nr/+N/ThZ/JNQPP9p5b3jufOfwW73jMFz9w70++6glf/NWvf3zbEdvOzU3P25I/Pv3f8v7X/0E7bPt5P973ikuPSf+3BwDAdPMZxr4myZXTtk0tlzj9v69bk+w7bFEAAMDKsqvV1vvHLclj0o0Me3m6xYqmbE1y/J22fvWUVD3y0It+/KevetcJG6Z2XlVrLlmTdtKa1l6U1s4a32cBAKx08wk7v5Pkbkn+OUmqam2S+yb5emvt3Gltr5vkvJFUCAAArARTq63PppIcmeT8JBszbU7PLSccuyNJ0tp/JPmPVN2iP9/l69rOz6e1i8ZXOgAwKeYTdr4xyUuq6mtJPpHkN5IclOTvZ2h7dJJvLrw8AABghZh1tfXp7fpg87Rdtmrta0m+tsCaAIBVZj5h56uS3D/J36QbglLphq+/dLBRv2jRryT5ixHVCAAALH+zrrY+ZDsAgHmbc9jZWrsyyYOq6k5JbpTk+621T83QdH2Sxyb52GhKBAAAVoDT0829eXiuOWdn0nWY2Nq3AwAYi2pt+kKI7EpV7ZdkW5INrbXtS10PAAAsFwOrsSc/H3hO/dFx3JYTjj1lcasCAJabceZra0Z5MgAAYPXqg8zjkkxfMX1rBJ0AwCLQs3Oe9OwEAIBd27hp89rMtto6ALDqjTNfE3bOk7ATAAAAAIZnGDsAAAAAwG4IOwEAAACAibBuIQdX1fokv5DkoCT/01o7fyRVAQAAAADM09A9O6vqj9JNNv7xJKckuW2//cCqOr+qfms0JQIAAAAA7N5QYWdVPSnJK5K8P8lvJ6mpfX3vzo8kefQI6gMAAAAAmJNhe3b+aZJ3tdYem+Q9M+z/bJJbDV0VAAAAAMA8DRt23jjJ+3ax/ydJDhjy3AAAAAAA8zZs2HlhkgN3sf+WSc4Z8twAAAAAAPM27Grs703y5Kp61fQdVXWrJL+b5PULKQwAALimjZs2r01ydJJD0y0YevqWE47dsbRVAQAsD9Vam/9BVYcl+XS6hYnek+TJSf4tydokj0j3S9dd+sWKJkpV7ZdkW5INrbXtS10PAACrx8ZNmx+e5MQkRwxs3prk6VtOOPaUpakKAGB+xpmvDRV29kUdlORFSR6eZP9+80VJ3pFkU2vtR6MocLkRdgIAsBT6oPPk/mEN7Jr6hf44gScAsBIsy7Dz505Sdb1083+e11rbueATLmPCTgAAFls/dH1LksPz80HnlJauh+dRhrQDAMvdOPO1YRco+jmttfNaa+dOetAJAABL5Oh0Q9dnCjrTbz+ybwcAsGoNFXZW1Qur6gu72P/5qnru0FUBAACDDh1xOwCAiTRsz87jkrxvF/vfm+TXhzw3AADw884ecTsAgIk0bNh5/STf2cX+7yW5wZDnBgAAft7p6ebknG3C/ZbkzL4dAMCqNWzYeXF2HWYeleSyIc8NAAAM6Bcdenr/cHrgOfX4eIsTAQCr3bBh52lJnlJVh0/fUVVHJnlyklMXUBcAADBgywnHnpJuOqmzpu3amuS4fj8AwKpWrc02EmYXB1XdLMkZ6f6L/LokX+l33TrJb6VbDfJurbWvjajOZaOq9kuyLcmG1tr2pa4HAIDVZeOmzWvTrbp+aLo5Ok/XoxMAWEnGma8NFXb2Rd02ySvT/aI16GNJ/qi19qUF1rYsCTsBAAAAYHjjzNfWDXtgH2beu6oOTHLDfvN3W2vnj6QyAAAAAIB5GDrsnNKHmwJOAAAAAGBJDR12VtXaJL+crlfnddLN0zmotdb+agG1AQAAAADM2VBhZ1XdKck7khyRa4acU1oSYScAAAAAsCjWDHncq5JcK8lDk1y3tbZmhtvakVUJAAAAALAbww5jv22SP2+tvWeUxQAAAAAADGvYnp1bM/vwdQAAAACARTds2Pm3SX63qvYbZTEAAAAAAMMadhj7vkkuTvLtqnpbkjOT7JjWprXWXr6Q4gAAAAAA5qpaa/M/qGrnHJq1SVykqO/Nui3Jhtba9qWuBwAAAABWknHma8P27DxqlEUAAAAAACzUUGFna+37oy4EAAAAAGAhhu3ZmSSpqsOT3CvJQUne0VrbWlVrk2xIsq21Nn0eTwAAAACAsRhqNfbqvCzJ95K8OcnLkty0371Pki1JnjaKAgEAAAAA5mKosDPJnyV5epKXJnlAkpra0VrbluSUJI9YcHUAAAAAAHM0bNj5u0ne1Fp7dpIvzLD/S/lZT08AAAAAgLEbNuw8MskndrH/p0n2G/LcAAAAAADzNmzY+aN0geds7pjkB0OeGwAAAABg3oYNO09J8ntVdcOBbS1JquqXkjwxyX8srDQAAAAAgLmr1tr8D6rakORjSY5KcnqSByb573Qrsd89yeeT3Ku1dsnoSl0eqmq/JNuSbGitbV/qegAAAABgJRlnvjZUz85+xfW7JXlxksOTXJbk3kn2T/L8JEdPYtAJAAAAACxfQ/XsXM307AQAAACA4S27np0AAAAAAMvNumEOqqrXz6FZa6399jDnBwAAAACYr6HCziT3Tb/6+oC1SQ7t789L8tMF1AUAAAAAMC9DhZ2ttY0zba+qPZI8JcnxSR4wdFUAAAAAAPM00jk7W2tXttb+IckHk/zDKM8NAAAAALAr41qg6ItJ7jWmcwMAAAAAXMO4ws4HJLlkTOcGAAAAALiGYVdjf84su/ZP16PzF5KcMGRNAAAAAADzNuxq7M+bZfsFSb6T5PeSvHbIcwMAAAAAzNuwq7GPa/g7AAAAAMBQhJYAAAAAwESYU8/Oqrr+MCdvrf1gmOMAAAAAAOZrrsPYtyRpQ5x/7RDHAAAAAADM21zDzieNtQoAAAAAgAWaa9h5QZLPtNZ+OM5iAAAAAACGNdcFiv4zyTFTD6rqu1X14LFUBAAAAAAwhLmGnRcl2X/g8cYk+4y6GAAAAACAYc11GPsZSf68qg5Osq3f9qtVdcgujmmttZcvqDoAAAAAgDmq1na/yHpV3TjJm5Lcrd/UktRuDmuttYlbjb2q9ksX+G5orW1f6noAAAAAYCUZZ742p56drbVvJ7lHVe2V5KAkW5Icn+RdoywGAAAAAGBYcx3GniRprV2W5AdV9fwkH2mtfX88ZQEAAAAAzM+8ws4prbXnj7oQAAAAAICFGCrsTJKqukWSJyW5YZLr5JpzeLbW2v0WUBsAAAAAwJwNFXZW1W8meUOSK5N8I8kFMzVbQF0AAAAAAPMybM/O5yX5fJJfaa2dP7pyAAAAAACGs2bI4w5L8npBJwAAAACwXAwbdn4pXeAJAAAAALAsDBt2/kmS366qe4yyGAAAAACAYQ07Z+czk2xLcnpVfTXJD5LsmNamtdYespDiAAAAAADmatiw87ZJWrqQc58kt5yhTRu2KAAAAACA+Roq7GytbRxxHQAAAAAACzLsnJ1LrqquW1VvrqrtVXVhVb2uqvbZzTGnVVWbdvvnxaoZAAAAABifYYexLwdvTnJokgck2SPJG5K8Jsljd3Pca5M8Z+DxJWOpDgAAAABYVHMOO6vqosxvHs7WWtsw/5LmVMstkjwwyZ1ba5/ptz0tyXur6hmttR/u4vBLWmvnjKMuAAAAAGDpzKdn5zuyfBYdunuSC6eCzt6HkuxMctck/7mLY3+jqh6X5Jwk70nyV621WXt3VtX6JOsHNu07dNUAAAAAwNjMOexsrT1xjHXM1yFJfjS4obV2VVX9pN83m7ck+X6SH6ZbUf5vk9wsycN3ccyzkjx3QdUCAAAAAGO3rObsrKoTkjxzN81uMez5W2uvGXj45ao6O8mHq+pGrbXvzHLY3yR52cDjfZNsHbYGAAAAAGA8llXYmeTvkpy0mzbfTTcE/aDBjVW1Lsl1+31z9en+/sZJZgw7W2uXJ7l84DrzOD0AAAAAsFiWVdjZWjsvyXm7a1dVn0yyf1XdsbX22X7zfZOsyc8CzLm4fX9/9nzqBAAAAACWnzVLXcAwWmtfS/L+JK+tqrtU1T2T/EOSt02txF5Vh1fV16vqLv3jG1XVX1bVHatqY1U9OMmbknystfalpfpcAAAAAIDRWJFhZ+83knw9yYeTvDfJx5M8eWD/HukWH9q7f3xFkvsn+WB/3N+lW2H+QYtULwAAAAAwRtVaW+oaVpSq2i/JtiQbWmvbl7oeAAAAAFhJxpmvreSenQAAAAAAVxs67KyqX6mq/66qH1fVVVW1Y/ptlIUCAAAAAOzKUGFnVT0iyX8lOTjJ2/rzvLX/+NIkX0ryghHVCAAAAACwW8P27HxWkjOS3CHJc/ttr2+t/UaSWyc5NMn3Fl4eAAAAAMDcDBt23jLJ21prO5Jc1W/bI0laa1uSvCrJMxdcHQAAAADAHA0bdl6S5Iokaa1dmOTydL05p5yb5KgFVQYAAAAAMA/Dhp3fSNe7c8oXkvxmVa2rqr2SPDbJDxZYGwAAAADAnA0bdv5nkodU1fr+8V8nOSbJhUnOS3J0khMWWhwAAAAAwFxVa200J6o6Oskj0s3hubm1dupITrzMVNV+SbYl2dBa277U9QAAAADASjLOfG3dqE7UWjs9yemjOh8AAAAAwHyMLOysqr2TPDrJ+iTvba19f1TnBgAAAADYnaHCzqp6XZK7ttZu3T/eM8mnkty6b7Ktqu7bWvv8aMoEAAAAANi1YRcouk+SUwYePzZd0Pkb/f05SZ67sNIAAAAAAOZu2LDzkCRbBh4/NMlnWmtvba19Nclrk9x1YaUBAAAAAMzdsGHnT5PsnyRVtS7JMUk+MLD/oiQbFlIYAAAAAMB8DLtA0eeS/G5VnZrkwUn2TfKegf03SnLuAmsDAAAAAJizYcPOP0/Xk/MzSSrJya21Mwb2PyzJ/yywNgAAAACAORsq7Gytfaaqbp7kHkkubK19dGpfVe2f5FVJPjrL4QAAAAAAI1ettaWuYUWpqv2SbEuyobW2fanrAQAAAICVZJz52lALFFXV9avqF6dtu11Vvamq3l5VDx1JdQAAAAAAczTsnJ1/n2SfJPdPkqo6OMmpSfZMtxL7cVX1yNbaKSOpEgAAAABgN4bq2ZnkLkn+e+Dx45NcK8ntkhye5MNJnrGw0gAAAAAA5m7YsPO6SX408PjXkny0tfad1trOJKckuflCiwMAAAAAmKthw87zktwguXr19bsl+cDA/nUZfog8AAAAAMC8DRtIfijJH1XV9iTHpAtN3zmw/5ZJzlxQZQAAAAAA8zBs2LkpyU2TvDTJFUme0Vr7XpJU1fokj0rylpFUCAAAAAAwB0OFna21c5Pcs6o2JLm0tXbFwO41Se4XPTsBAAAAgEW0oHk1W2vbZth2aZIvLuS8AAAAAADzNaews6oe33/4r621NvB4l1prbxq6MgAAAACAeajW2u4bVe1M0pJcq7V2Rf94d1prbe1CC1xuqmq/JNuSbGitbV/qegAAAABgJRlnvjbXYexHJcnA3JxHjbIIAAAAAICFmlPY2Vr7/q4eT1dV106yYQF1AQAAAADMy5oxnff4WI0dAAAAAFhE4wo7AQAAAAAWlbATAAAAAJgIwk4AAAAAYCIIOwEAAACAiTCn1diTpKp+YR7nPWyIWgAAAAAAhjbnsDPJZ5K0ObatebQFAAAAAFiw+YSdTxpbFQDA8lK1Z5KHJrl3kr2S/CDJv6a17y5lWQAAALtSremAOR9VtV+SbUk2tNa2L3U9ADByVY9KcmKSQ366x15bLt1j/Y79L73okLVt596V/GeS305rFy5tkQAAwEo1znxtPj07AYBJV/WbSd70/f0P+eTvP/RZ+erBN9qYJHtdeVke+eUPX/CXH37tL+2586oPp+qYtHbRktYKAAAwjZ6d86RnJwATq+qAJFu/dcCRn3zAb7/qmFQl3TzcU9otfvTdvOek4y9f13a+PK09e2kKBQAAVrJx5mtrRnkyAGBFe1JL1jzmMS+62QxBZ5LU1w66Yd5+21+6siW/k6r1S1AjAADArISdAMCUB5+7zwGfOv/a1zks1ww6p9Sb7/Cr+1ZyvSR3W8TaAAAAdkvYCQBM2XDetfe/fHeNfrTPda5uP95yAAAA5kfYCQBMOf/gi3+87+4aHXnhuVMf/ni85QAAAMyPsBMAmPIf1/vphXc58sJzzk4y2wqG7fGf+6+ftuQHST61iLUBAADslrATAJjy5kq2v+2tz/rhuh1XJdcMPNvdv/+lPOSrH11fyT+ltR1LUCMAAMCsqrXZOm4wk6raL8m2JBtaa9uXuh4AJljVmiR3TXJYkkuTfDKtXTDmaz4wybvP23v/7zz7gX944IdufJcDW63JQRf9OE/87Hu2P+WMd1xrbWunJnlQWrtirLUAAAATaZz5mrBznoSdAIxdF3L+fpLjk9x4YM+lSd6W5DlpbesYr3/vJK9Icvuras1Pr1y7xxV7XXX5hiSXVvIvSZ6Z1na7kBEAAMBMhJ3LiLATgLHqgs7XJ3lCkrdetm7P1zzm0S/a5zqXbr/h0z7xttvc/uxv/lolO5PcO619e4x1VJK7Jbl3kr2SnJnk5LS2bWzXBAAAVgVh5zIi7ARgrKr+KF2vysdtfOZ/XZbkxCRHTO0++KIf//BD//J72feKSy9Mcpu0tnNJ6gQAABjSOPM1CxQBwHJRtTbd0PV/64POk5McPtjk3H0POPSJj3z+oUlumeQBi14jAADAMibsBIDl4xeTHHXpuvWvTtejM0lqWpv67OG3yDcPOPLKnaknLmp1AAAAy5ywEwCWj8OT5IG/9cp90g1dnx50dqrq/w658R7b9trnVotYGwAAwLIn7ASA5eOyJDno4gtuuLuG+15+SS7dY/34KwIAAFhBhJ0AsHz8T5Irn/GxN+2yx+b+l27P0Vs+n3P3OeBTi1QXAADAiiDsBIDlorVzk7zjLlu/8qDrXrLth0naTM2e+qn/yNqdO3LIxec/Z3ELBAAAWN6EnQCwvPx5JXt/5LVPueJW534nGQg897vs4mw67Q158hn/mU9e/7ZvOnT7+ecsXZkAAADLT7U2Y6cRZlFV+yXZlmRDa237UtcDwASqunWSdyW54ZcPvtEVXzn4Rnvud9nFuc93P5s9dl6VT17/Nv969JYvPCF+iAMAACvQOPM1Yec8CTsBWBRV65I8aGfy+IvWX/tml+6xPufuc8AnD7n4x3958EU//uFSlwcAADAsYecyIuwEAAAAgOGNM18zZycAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAS6fqWqk6PFX7L3UpAAAArHzCTgAWX9XdUvWWJNuSbE1yQao+naonpGqPJa4OAACAFWrdUhcAwCpT9cdJXpbk21fVmj//z1vdZ22rOvK+3/nMnQ+85MKTkvxGqh6a1i5Z2kIBAABYaYSdACyequPSBZ0vvskz/vOMK9fu8YokR0ztvvd3P3ve605+/tHr2s7XJ3n0ElUJAADAClWttaWuYUWpqv3SDbvc0FrbvtT1AKwYVZXki0m23vDP3vUvO9esPXlqz0Cr9sgv/Xde8r4TK8kt0trXF71OAAAAxmqc+Zo5OwFYLHdNcpvL1+5x4s41a0/st9W0NvWuWx6TC/bad+eOqicvcn0AAACscMJOABbLLZPkrn/wxqvSDV2fHnQmSa5Yt0edceSt1vzkWhvusZjFAQAAsPIJOwFYLFPzphy2u4Zr2s5ctm7P9WOuBwAAgAkj7ARgsXwhSZ7z4dceuatG17ristz1zK9k+17X/r9FqQoAAICJIewEYHG09vkkn37YV0795TU7d2zNz3p6/pxHf+kDbZ/LL8mhF/34OYtbIAAAACudsBOAxfScSn7x/a9/2jfWX3VFMhh4tpZjv3Z6e9apb6hvXO8GH7zuJdu+t2RVAgAAsCJVazN2rGEWVbVfkm1JNrTWti91PQArTtXjkrzh8rV7XPLW2/1yfeXgG+67/6UX56FfPS23+tF3873rHPbxoy744X3T2pVLXSoAAACjN858Tdg5T8JOgBGoukmSp7bkcZUcuKPqqvP33v+M/S+7+K/X77jyffHDCQAAYGIJO5cRYSfAiFWtS7JDwAkAALA6jDNfWzfKkwHAvLV21VKXAAAAwGSwQBEAAAAAMBFWbNhZVX9eVZ+oqkuq6sI5HlNV9YKqOruqLq2qD1U3bxwAAAAAsMKt2LAzyZ5J/iPJP83jmP+X5I+S/F6Suyb5aZIPVNVeoy8PAAAAAFhMK3bOztbac5Okqp44l/ZVVUmOT/LC1tq7+m2PT3Jukocmeds46gQAAAAAFsdK7tk5X0clOSTJh6Y2tNa2Jfl0krvPdlBVra+q/aZuSfYde6UAAAAAwLytprDzkP7+3Gnbzx3YN5NnJdk2cNs6+tIAAAAAgIVaVmFnVZ1QVW03t5svcll/k2TDwO2IRb4+AAAAADAHy23Ozr9LctJu2nx3yHOf098fnOTsge0HJ/nCbAe11i5PcvnU427qTwAAAABguVlWYWdr7bwk543p9N9LF3jeL3242c/BedfMb0V3AAAAAGAZWlbD2Oejqq5fVbdPcv0ka6vq9v1tn4E2X6+qhyVJa60leUWSv6iqB1fVbZK8KckPk7xzsesHAAAAAEZrWfXsnKcXJHnCwOPP9/f3SXJa//HN0s2zOeXFSa6d5DVJ9k/y8SQPbK1dNs5CAQAAAIDxq67DI3PVD33flmRDa237UtcDAAAAACvJOPO1FTuMHQAAAABgkLATAAAAAJgIwk4AAAAAYCIIOwEAAACAiSDsBAAAAAAmgrATAAAAAJgIwk4AAAAAYCIIOwEAAACAiSDsBAAAAAAmgrATAAAAAJgIwk6AUauqVN0tVSel6gup+mKq3pqqY1JVS10eAAAATCphJ8AoVe2b5D1JPtmSe333Oodv+fLBNzr3p3vsdY8kpyY5NVUHLG2RAAAAMJmqtbbUNawoVbVfkm1JNrTWti91PcAyUrUuyfuT3OUtt3vgP//5Lz31MW3NmiOSJK3l/t/+9Pn/+K6/3XP9jiu/keTeae3SpSwXAAAAlsI48zU9OwFG5+FJ7vcPd3/US5/9wD98Rluz5vCr91TlQze52wGPeNyL923JHZM8YcmqBAAAgAkl7AQYnae25GMvvdfjf7d/PH1+zvq/Q26S0254p8tb8lTzdwIAAMBoCTsBRqFqTZKjP3n923whyRG5ZtB5dcv/vNUx16rkNkk2LFZ5AAAAsBoIOwFGY22SNWfve709d9fwkj2uNfXh+rFWBAAAAKuMsBNgFFq7Msk5tzv7mwftrumtzv1OdlRdluSC8RcGAAAAq4ewE2B0TrrRT7be79qXX3JWkjZTg3U7rmq/8YX37aiWf01rVyxyfQAAADDRhJ0Ao/NPlezx/tf/4Q/3vOrKZFrguXbnjvbi951YB/70wrYm7e+XpkQAAACYXNXajJ2PmEVV7ZdkW5INrbXtS10PsMxU/WqSd2xfv/f5r7jnY6/9wZve/To7q3LPLV/Mb//vO6+86fnfX7smeVxae+tSlwoAAABLYZz5mrBznoSdwG5V3THJX7TkwdX3oG9Ja8n71yQvSmsfX+IKAQAAYMkIO5cRYScwZ1WHJ7lVkkryjbS2ZWkLAgAAgKU3znxt3ShPBsCA1s5KctZSlwEAAACrhQWKAAAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIqxb6gKAZaxqTZJ7JzkqyZVJPpXWvrW0RQEAAADMTNgJXFNVJXlKkj9LcsNp+z6Y5Nlp7bNLUBkAAADArAxjB35eF3S+PMk/JfnUpevW/+Jtnv72+9/z9173pP++8V1e1JJDkpyeqvssbaEAAAAAP69aa0tdw4pSVfsl2ZZkQ2tt+1LXAyNX9etJ3pbkqRuf+V/nJjkxyRFTu/e68rKzTn3NU84/9OIf3yDJxrS2bYkqBQAAAFagceZrenYC0x2f5EN90HlyksMHd162x16HPeTxL7vtztS+SR6/BPUBAAAAzEjYCfxM1Y2S3O2KNetek65HZ5LU9FY/2veAnHqjO13RksctboEAAAAAsxN2AoMOTZIX3ee3rpVu6Pr0oHNKffWgG17r8rV7bFyswgAAAAB2R9gJDPppkqy/6oqjdtdww2UX54p1e141/pIAAAAA5kbYCQz6vyTn/PqX/vsOu2q051VX5le/8fH8aJ/rfHKR6gIAAADYLWEn8DOtXZnkNUddcNb9b/ajLeckaTM1+63PvLMdeMm2HHzRT567uAUCAAAAzE7YCUz3skq++643/fFeD/jWp7Jm546rA8/9Lrs4f/qxf82mj76xPnfYzU/Z9/KffmUpCwUAAAAYVK3N2HGLWVTVfkm2JdnQWtu+1PXAWFRdL8m/Jznmh/seuOOLh9507fqrrsg9fvClrN25I5877Ob/ftetX3l0vIEAAAAA8zTOfE3YOU/CTlaVqjvvTP3OT/be7w6X7LHX2h/vveFzh1704+ccctH5Zy91aQAAAMDKJOxcRoSdAAAAADC8ceZr5uwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACaCsBMAAAAAmAjCTgAAAABgIgg7AQAAAICJIOwEAAAAACbCuqUuACZC1bokxyY5JsleSbYm+be09v2lLAsAAABgNdGzExaq6sFJvpvknZesW//In1xrvwdeVWue3ZLvpurtqdqw1CUCAAAArAZ6dsJCVD0yydu37ne9zz7twc/c8/OH3/zwJNn7ikvzqC/99wV/furrj91j51UfTtUxae3ipS0WAAAAYLJVa22pa1hRqmq/JNuSbGitbV/qelhCVfsn2fq96xz2+fv+7j/fs9WaJKmBFu2W5343737j8ZevaztfkdaetSR1AgAAACwj48zXDGOH4T2+Jesf85gX3XCGoDNJ6qsH3zBvvd0vX9mS30nVXktQIwAAAMCqIeyE4T30R9e+zhnn7HvgYblm0Dml3nyHX923kgOT3GMRawMAAABYdYSdMLwN5137OpfurtE5+x4w9eH+Y60GAAAAYJUTdsLwfnLwxT/Zd3eNjtj2o6vbj7ccAAAAgNVN2AnDO/nASy6805EXnnN2ktlW+mq/+bnNF7fk7CT/s4i1AQAAAKw6wk4Y3psr2f7Wtz7rrHU7rkquGXi2O279ao77vw+tr+TVae3KJagRAAAAYNUQdsKwWrs4yWOP2H7e7T75T0/82n2+87/nV9uZJLnuJdty/MffvP3f37LpyrWtfSLJ3y5prQAAAACrQLU22+hbZlJV+yXZlmRDa237UtfDMlB1TJK/T3KbK9es3X7F2j2uuNaVl+9faVdVclKSP0lru13ICAAAAGA1GGe+JuycJ2EnM6qqJPdMct8keyU5M8m/p7UfL2ldAAAAAMvMOPO1daM8GawoVWuS3C/Jk5Pcpt/6f0leneTDaf2Y9Lno/mvw8f4GAAAAwBIwZyerU9UBSU5L8sGW3PybB17/i5877GbfvnjPa/1Ckg8mOa1vAwAAAMAKYRj7PBnGPgGq9koXdN7on+76iH/823s/8bdTdUSSpLXc57ufOe+f//NF69fvuPLrSe6d1i5bwmoBAAAAJso48zU9O1mNHpvkLi+8z2+/5G+PedJzUnX41XuqcuqN7nzgcb/x4n1bcue+LQAAAAArgLCT1ej3dybv+5e7POxp/eOatr++fOhN8j83uP3lLXnqYhcHAAAAwHCEnawuVeuT3OmjN7zj/yU5ItcMOq9u+a5b3nuvSu7YD3sHAAAAYJkTdrLa7JkkZ+974LV21/DSPdb/3DEAAAAALG/CTlabi5P85I5nfe3g3TW87dnfypVr1l7UHwMAAADAMifsZHVprSV5w03P/8H9977i0rOStJma7XXlZe3Xv/TBnWvaztemtZ2LWyQAAAAAwxB2shq9qpJrfeD1f3DWnlddmUwLPPe86sp24nteWvtccelVa1v7x6UpEQAAAID5qq6jG3NVVfsl2ZZkQ2tt+1LXw5Cqjk1y8kV7Xusnr7zHo/f+8I3vsn+S/OKWL+S3PvOuq47Ydm5b29rD09p/LW2hAAAAAJNlnPmasHOehJ0TpOoOSTa15OGVrEuSncmOpE5Zk/Y3ae3zS1whAAAAwMQRdi4jws4JVHVQkpv1j76Z1s5dynIAAAAAJtk487V1ozwZrEit/SjJj5a6DAAAAAAWxgJFAAAAAMBEEHYCAAAAABNB2AkAAAAATARhJwAAAAAwEVZs2FlVf15Vn6iqS6rqwjkec1JVtWm394+5VHalal2qHp6qD6Xq4lRdlqqvpupPUnXdpS4PAAAAgJVjxYadSfZM8h9J/mmex70/yaEDt8eMuK6Vq+rAVD04VY9K1d1TNd7nR9X+ST6U5B0tudZnDr/Fv77/Jnd/29b9rndWS/4myddTdeex1gAAAADAxFi31AUMq7X23CSpqifO89DLW2vnjL6iFaxqY5K/SvKodCHylG+l6uVJ/jmttRFfc02SdyS53d/f49HPednRj3tykntM7T7kovN/uPmkp198wCXb3p+qO6W17430+gAAAABMnJXcs3NYx1TVj6rqG1X1T1V1wK4aV9X6qtpv6pZk30Wqc3FU3TLJp5Mcc1Wt+cs/ePAzH3Xf3/nn337dnR7y9J2pzyd5VZJXp6pGfOUHJLnva+7ysFe+7OjHPT/J4YM7z9n3wEPv87v/fJPL1u6xJskzRnxtAAAAACZQjbrD3mLre3a+orW2/xzaPjrJJUm+l+RGSV6U5OIkd2+t7ZjlmOclee4Muza01rYPV/UyUbUuydeTXPLgx7/s77506E1fmOSIgRZb//7dL37ng7/2sT9M8ntp7dUjvPY7W7LxqP/3ngNSdXiSmcLUtunU11/0lDNOWVPJoWnt4pFdHwAAAIAl0Xco3JYx5GvLqmdnVZ0wwwJC0283H/b8rbW3tdbe3Vr7cmvtnUl+Lcmdkxyzi8P+JsmGgdsRu2i70jwoyY2ee/+nvPFLh970DZnWuzLJ4X/04P/3B1v2P/QTSf54xL077/jVg2745VQdkZmDziSp993snvtVsk+SG4/w2gAAAABMoGUVdib5uyS32M3tu6O6WGvtu0nOzy6CtNba5a217VO3JBeN6vrLwONb8uk33vFBx/ePp4eOlSR/+Uu/f+MkN0tylxFee822vfa51u4aXbVm7dSHK3Z+WQAAAAAWx7IKkFpr5yU5b7GuV12vwgOSnL1Y11xmDv/+/oeem+Suu2hTXz7kxgf1Hx+Rbn7PUfjmzc/bcsPdNbrrmV/JztRVa2KBIgAAAAB2bbn17Jyzqrp+Vd0+yfWTrK2q2/e3fQbafL2qHtZ/vE9VvaSq7lZVG6vqfkneleTbST6wFJ/DMnDZjjVrDtxdo30uv2Tqw0tHeO3XXPfS7Xe49TnfPjfJjBPH7rHjyvbEz77nqiTvSGs/HuG1AQAAAJhAKzbsTPKCJJ9P8vx0czp+vr/daaDNzdLNs5kkO5LcNsm7k3wzyeuSfDbJ0a21yxep5uXmIxsvOPv2A2HmjB78tY9lR9UVST41wmu/I8nX3/aWTe2Ibecm0wLPPXZc2V72Xy+rI7afmzVpLxnhdQEAAACYUCt+NfbFNs7VohZd1eEt+f6/3Plhl/z1fX97n8ywUNCBP72gve/1T9t5wCXb/nVN2/mkEV//qCQfuqrWHP6uW977qvfc4l7XvmLtHrnDD7+R3/z85h0H/fSCtqa1X09rp4z0ugAAAAAsmXHma8LOeZqosDNJqv48yQv/8W6PzKvv+oi2fa99rg48b/fDb7SXvvcVdeS2cy/Y66orbpfWzhzD9a+b5KkteUr1K93vqLq8Wt68Ju0Vae3LI78mAAAAAEtG2LmMTGDYWUmetTP1gsvX7bHm1BveqS5ev3du8aPv5TbnficX7bn3Wfteccl909o3x1zH2iSHJ9kjyTlp7adjvR4AAAAAS0LYuYxMXNg5perQHbXmydv22uehO2rNta9au/aH171k29+t33HVe9PajqUuDwAAAIDJIOxcRiY27AQAAACARTDOfG0lr8YOAAAAAHA1YScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATQdgJAAAAAEwEYScAAAAAMBGEnQAAAADARBB2AgAAAAATYd1SF7CC7VtVS10DAAAAAKw0+47rxMLO+Zv6Zmxd0ioAAAAAYGXbN8n2UZ6wWmujPN/Eq64752FJLlrqWibIvunC4yPi6wrLidcmLE9em7A8eW3C8uN1CcvT1Gvz5km+2UYcTurZOU/9N+Cspa5jkgxMB3BRa22kaT4wPK9NWJ68NmF58tqE5cfrEpangdfm2aMOOhMLFAEAAAAAE0LYCQAAAABMBGEny8HlSZ7f3wPLh9cmLE9em7A8eW3C8uN1CcvTWF+bFigCAAAAACaCnp0AAAAAwEQQdgIAAAAAE0HYCQAAAABMBGEnAAAAADARhJ0suqo6tKpOqKpTq+qiqmpVdcw8jn9ef8z022Xjqxom30Jfm/05Dq+qf6+qC6tqe1W9q6puOJ6KYfWoqv2r6jVVdV5V/bR/nf7CHI89aZafm18fd90wCapqfVX9bVX9sKourapPV9UD5nisn4swBsO+Lv0tCeNVVftU1fOr6v1V9ZP+9fXEeRw/9O+8g9bN9wAYgZsleWaSbyX5cpK7D3me309y8cDjHQusC1a7Bb02q2qfJKcm2ZDkRUmuTPLHST5aVbdvrf14tOXC6lBVa5JsTnK7JC9Jcn6SpyY5raru2Fr71hxOc3mS35m2bdtIC4XJdVKS45K8It3PyCcmeW9V3ae19vHZDvJzEcbqpAzxuhzgb0kYjwOTPCfJD5J8Mckxcz1wRL/zJhF2sjQ+m+SA1tpPquq4JP8x5HlObq2dP8K6YLVb6GvzqUlukuQurbX/TZKqel+S/0vyp0mePcpiYRU5Lsk9kjyytXZyklTVvyf5ZpLnJ3nsHM5xVWvt38ZXIkymqrpLkkcn+bPW2kv7bW9K97Ptxelem7PxcxHGYIGvyyn+loTxODvJoa21c6rqTkn+dx7HjuJ33iSGsbMEWmsXtdZ+MoJTVVXtV1U1gnPBqjeC1+ZxSf536g+6/pxfT/LhJI9aaH2wih2X5Nwkp0xtaK2dl+TfkzykqtbP5SRVtbaq9htPiTCxjkvX4+s1Uxtaa5cleV2Su1fVkbs51s9FGL2FvC6n+FsSxqC1dnlr7ZwhDx/J77yJsJOV7bvphuBdVFX/VlUHL3VBsFr1Qw5um+QzM+w+I8mNqmrfxa0KJsYdknyutbZz2vYzkuyd5KZzOMfeSbYn2dbPn/SP/RBbYNfukOSbrbXt07af0d/ffqaD/FyEsRrqdTmNvyVh+RnF77xJDGNnZbogyT8k+WS6OciOTvIHSe5SVXea4YceMH7XTbI+3bCF6aa2HZbkG4tWEUyOQ5N8bIbtg6+tL+/i+LPTDev7XLp/dD8w3fDa21XVMa21q0ZYK0yaQ7P7n20z8XMRxmfY12Xib0lYzhb6O+/VhJ0sSP9f6z3n2Pzy1lpb6DVbaydO2/SOqjojyZvT/fF2wkKvASvdErw2rzV1rhn2XTatDaxaQ742r5UFvLZaa8+atultVfXNJH+dbrjQ2+ZYD6xGw77+/FyE8Rn656K/JWFZW9DvvIMMY2eh7pXk0jnebjauIlprb0lyTpL7j+sasMIs9mvz0v5+pnlU9prWBlazYV6bl2b0r62XJ9kZPzdhd4Z9/fm5COMz0p+L/paEZWNkr209O1moryd50hzbzjTUYJTOTDdkCFj81+ZP0v0X7tAZ9k1t++EIrgMr3TCvzbMz4tdWa+3Sqvpx/NyE3Tk7yeEzbN/d68/PRRifYV+Xu+JvSVh6I/udV9jJgvSrbJ201HX0q+htTPL5JS4FloXFfm221nZW1ZeT3GmG3XdN8t3W2kWLVQ8sV0O+Nr+Q5OiqWjNtwva7JrkkyTfnW0e/MMqBSc6b77GwynwhyX2qar9pc/nddWD/Nfi5CGP1hQzxupyNvyVh2fhCRvQ7r2HsLGtVdf2quvm0bdeboenvJ7lekvcvSmGwys302kxycpI7V9WdBtrdLMl9k/zHYtYHE+bkJAcnefjUhqo6MMkjk7yntXb5wPYbVdWNBh7vNcuKz3+ZpOLnJuzOyUnWJnny1IaqWp+uh/anW2tn9tv8XITFM/Tr0t+SsDxU1aFVdfOq2mNg85x/593t+UewXgzMW1X9Rf/hrZI8Osnrk3wvSVprLxxod1qSe7fWamDbJUnenm4VrsuS/GJ/ji8muWdr7ZJF+BRgIi3wtblvuv+I75vkpUmuTPIn6X4ZvX1rTQ8yGEJVrU3y8SS3TvKSJOenW0Th+knu3Fr7xkDbLUnSWtvYP96Y7nX51nRD6JPkl5P8aro/6o6d9p9zYJqq+vckD0s31+23kzwhyV2S3K+19rG+zWnxcxEWzQJel/6WhDGrqj9Msn+61dN/P8kp+VnP6Ve21rZV1UnpXrdHtda29MfN+Xfe3dYg7GQpVNWsT7xpP4xOyzV/QL02yT2SHJluotrvJ3lHkr82HAgWZiGvzX77Eel+6fyldKMHTkvyx621b4+jXlgtquo66X7pe2i6lSj/N8kzWmufmdZuS/JzYef+SV6Z5G7pfuFcm+6PwjcneWlr7crFqB9WsqraK8lfJXlckusk+VKSv2ytfWCgzWnxcxEWzbCvS39Lwvj1v4/eYJbdR7XWtswUdvbHzul33t3WIOwEAAAAACaBOTsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAAAAAJoKwEwAAAACYCMJOAAAAAGAiCDsBAAAAgIkg7AQAWMaq6nlV1Za6jnGrqnVV9eKqOrOqdlbVO/vtraqet7TVAQCwUgg7AQAWSVU9sQ/vpm6XVdUPq+oDVfVHVbXvUtc4par27oPWY+bY/pj+czpuyEv+VpI/S3JykickefmQ51nxquoXq+p9VXVW/xz5QVW9p6oeu9S1AQAsd+uWugAAgFXoOUm+l2SPJIckOSbJK5L8SVU9uLX2pYG2L0xywmIXmGTvJM/tPz5tEa533yRntdb+eBGutWxV1SOTvD3JF5KcmOSCJEcluVeS303yliUrDgBgBRB2AgAsvve11j4z8Phvquq+Sf4rybur6hattUuTpLV2VZKrdnWyqlqTZM/W2mVjq3j8Dkpy4VIXsQw8L8lXk9yttXbF4I6qOmixiqiqSrLX1PMQAGClMIwdAGAZaK19JMlfJblBksdNbZ9pzs5+uPg//P/27j7YqqqM4/j3d9Fq1OLiyzhajRQyOqVW1tjL2AtgITbZGJEzkYwF+TaNIL0QKWHOhKJhjjnmLRAEGolR1LKSdAYJcSolU4EUvILBGIKAJPGS4dMfax3Ysznn3nOv995OZ36fmTv77r3XXmvtdc4fZ55Zaz2SRktaBewFzs733i7pdkkvSdoraZWkr5Xbk/SWXPeavFT6H5IWSRokaSCwJRedWlh2f3VX3qnSd0knSpoj6RVJOyTNlnRYLjMwv98Q4L2Ftj5Vo845ktbXaqvK9a9IWiFpt6RtkhZIemepzMOSVkp6j6QlknblJeTf6cq4Fcq0SJqQx35P/izaJA2oY9gGAY+VA50AEbG51JcWSeMlPZ3b2SLpAUkfKpQ5RNIUSe35+7Be0jRJby7VtV7S/ZKGS3oc2A1cnO+1Srop76e6V9JzkiblILuZmZlZQ/EPFDMzM7PGMS8fP1NH2aGkfS1/CYwH1ks6FvgjcBZwS77+HDBL0oTKg5L6kWaRTgVWAN8kLZnuD5xCCnRemovfA1yQ/xZ1870WAm8FJuf/L+TAEvktue5ngI2Ftv7Wzbb2k3QlMBdYC0wkbRUwDPiDpNZS8QHAA8CTpPF4BpguaUShvs7GraINuAFYTvoMZgOjgcWSDu2k2y8AwyS9o45XnJXfaQMwibTdwR7gI4UyM4FrgL8AVwBLSZ/Dgir1nQTcCTyY+/3XHJReSgrAzwUuz+91LXBjHX00MzMz61Nexm5mZmbWICJio6QdpNl9nTkJODUiVlcuSJoJ9MvXt+bLt0m6E7haUlteljyGFPSbGBHFREDXSVJEhKS7gJ8CT0XE/Df4ak9ExNhCP48CxgKTIuJfwHxJ44B9PdBWpY0TgB8AV0XEtML1RcATwGXAtMIjxwNjImJeLjeLFHgcC/wul+lw3PJzZwLjgNERsX9/TUlLSMHUUXS87+Z0UhCzXdJy4BHg98CjEfF6ob4hpKDxzRExvvD8jEJf3kdK9jQzIr6e798qaTPwLUlDImJJ4dkTgbMjYnGhnatI38cPRMTafLlN0ovAtyXNiIgNHbyPmZmZWZ/yzE4zMzOzxrKTNAuyM0tLgU4BI4Ff59OjK3/AYtLsw9Nz8ZHAy8BPypVGxEFLwXvAbaXzZcBRkt7WC21VfIH0W3dhaSw2kWZ6DimV3wnsD7TmZeR/Bt5dKFPPuI0CdgAPltpdkdsot1uu53bSlgQPA2cCU0jjtVbSx0p9CVJAt1ZfzsnH8gzMGfn42dL1dcVAZ+F9lgHbS+/zECmw/omO3sfMzMysr3lmp5mZmVljOQLY3GmplM296BigFbgo/1VTSXAzCHg2Jz/qC38vnW/PxwHAP3upzcGASIHNal4rnW+sEujdDpxWOK9n3AaTAsu1PsNOkwzlgOPivIT8g8D5wCXA/ZJOznt3DgJejIhtHVR1AvA6aSuDYv2bJL2S7xeVv1OQ3uc0DuzhWtZnSZPMzMzM6uFgp5mZmVmDyPs09qcUnKqhnCW7smJnPnBHjWee6mbX3qh9Na6rG3XVmnnar3TeksuOqNH+ztJ5T/WxhRToHF3jfq2g4UEiYhdpVuUySS+T9godQe3Pt2ZVdZarlnm9hbSH5/U1nlnTxb6YmZmZ9SoHO83MzMwaxwX5WF5KXI8twKtAv4h4qJOy7cCHJR0aEeUZjhW9sZy9J2wnzWAtK89SbCcFKtdFRE8F5OoZt3ZSgqjleX/UnvJ4Ph5XaGe4pCM7mN35AilYOZhCwqecyKo13+9MO3BEHd8pMzMzs4bgPTvNzMzMGoCkoaT9GdcBv+jq8xGxD7gbGCnplPJ9SccUTu8Gjga+UaVcZSbjrnxs7Wpfelk70F/S/uXlko4DziuVW0SarTm18E6V8spJkrqqnnFbSJplOqVKmUOqZIEvlxlW41Zl/81nC30RB7LaV+vLb/NxQqnIxHz8TUd9yRYCH5U0vEo7rZI8ecLMzMwain+cmJmZmfW9EZJOJv0WOxYYCnyaNNPu3IjY0816v0tKgPMnST8HVgNHkhITnZX/B5hLyix+o6QzSEulD89lbgXui4jdklYD50taA2wDVkbEym72racsIGUsv0fSzcBhwKWk5dSVBExERHvOJH4tMFDSvaSZr+8iBUZ/Bvyoi23XM25LJbUBkyW9n5RJ/TXS7MpRwHjgrg7auE/SOlKiqfZC/Z8DHsvXiYglkuYBl0saTMr03gJ8HFgC3BIRT0q6A7goB1mXAmeQMrTfW8rEXssNwLmk/ULnkBItHQ6cCnwRGEhK2mRmZmbWEBzsNDMzM+t71+Tjv0lBxKdJs+9mR8Sr3a00Il7KQbjvk7KRXwZsBVYBkwrl9kk6B7gS+DIps/dW4JHcl4pxpMzjPwbeRMr8/T8NdkbEVknnkTKMX0+aCTuZFEw8vVT2uhyovYIDMyA3kAKQv+pG23WNW0RcImkFcDEwDfgPsJ60n+ryTpoZB3we+BJwPGn25vPAD4HppeRIXyXtwzqWFJTcQVru/mipvueBC0lB3k2kAPBBWdxrvPMuSZ8EvkcK1o4hJZVaQxrTHfXUY2ZmZtZXdHDSSTMzMzMzMzMzM7P/P96z08zMzMzMzMzMzJqCg51mZmZmZmZmZmbWFBzsNDMzMzMzMzMzs6bgYKeZmZmZmZmZmZk1BQc7zczMzMzMzMzMrCk42GlmZmZmZmZmZmZNwcFOMzMzMzMzMzMzawoOdpqZmZmZmZmZmVlTcLDTzMzMzMzMzMzMmoKDnWZmZmZmZmZmZtYUHOw0MzMzMzMzMzOzpuBgp5mZmZmZmZmZmTWF/wJW/OKkxSTj8wAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -1045,9 +1013,6 @@
    "id": "4cd9e82d2c024051",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1060,8 +1025,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation Lissa vs direct 0.9994023305214189\n",
-      "Spearman Correlation Lissa vs direct 0.9972693150615916\n"
+      "Pearson Correlation Lissa vs direct 0.9996494901218611\n",
+      "Spearman Correlation Lissa vs direct 0.99770259717359\n"
      ]
     }
    ],
@@ -1142,7 +1107,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage error of Arnoldi over direct method:49.53099489212036 %\n"
+      "Percentage error of Arnoldi over direct method:33.655646443367004 %\n"
      ]
     }
    ],
@@ -1158,9 +1123,6 @@
    "id": "6b5b115c03594119",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1171,7 +1133,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -1215,8 +1177,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation Arnoldi vs direct 0.9869006830707897\n",
-      "Spearman Correlation Arnoldi vs direct 0.9775700934579439\n"
+      "Pearson Correlation Arnoldi vs direct 0.9900823761476388\n",
+      "Spearman Correlation Arnoldi vs direct 0.9823966546590421\n"
      ]
     }
    ],
@@ -1236,9 +1198,6 @@
    "id": "a4017f6afd3ebf93",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1251,11 +1210,7 @@
   {
    "cell_type": "markdown",
    "id": "a1c962f5fc8ae934",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
+   "metadata": {},
    "source": [
     "Similar to the Arnoldi method. the Nyström method uses a low-rank approximation, which is computed from random projections of the Hessian matrix. In general the approximation is expected to be worse then the Arnoldi approximation, but is cheaper to compute."
    ]
@@ -1266,9 +1221,6 @@
    "id": "f68a046f672bbfc1",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1281,7 +1233,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Encountered error in cholesky decomposition: linalg.cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 16 is not positive-definite)..\n",
+      "Encountered error in cholesky decomposition: linalg.cholesky: The factorization could not be completed because the input is not positive-definite (the leading minor of order 19 is not positive-definite)..\n",
       " Increasing shift by smallest eigenvalue and re-compute\n"
      ]
     }
@@ -1306,9 +1258,6 @@
    "id": "a1bd4c9f39629e5a",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1321,7 +1270,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage error of Nyström over direct method:27.906718850135803 %\n"
+      "Percentage error of Nyström over direct method:36.14298701286316 %\n"
      ]
     }
    ],
@@ -1337,9 +1286,6 @@
    "id": "d414f021ba9ca35e",
    "metadata": {
     "editable": true,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "slideshow": {
      "slide_type": ""
     },
@@ -1350,7 +1296,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -1394,8 +1340,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation Nyström vs direct 0.9963587721195153\n",
-      "Spearman Correlation Nyström vs direct 0.9899740534549211\n"
+      "Pearson Correlation Nyström vs direct 0.9977239930897606\n",
+      "Spearman Correlation Nyström vs direct 0.992110235030355\n"
      ]
     }
    ],
@@ -1475,7 +1421,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Percentage error of EK-FAC over direct method:1430.8037757873535 %\n"
+      "Percentage error of EK-FAC over direct method:1093.1286811828613 %\n"
      ]
     }
    ],
@@ -1509,7 +1455,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -1561,8 +1507,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation EK-FAC vs direct 0.9605675426898496\n",
-      "Spearman Correlation EK-FAC vs direct 0.9004156485376729\n"
+      "Pearson Correlation EK-FAC vs direct 0.9573912191268695\n",
+      "Spearman Correlation EK-FAC vs direct 0.8975136660201023\n"
      ]
     }
    ],
@@ -1603,8 +1549,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Pearson Correlation EK-FAC vs direct - top-20 influences 0.9890609121852287\n",
-      "Spearman Correlation EK-FAC vs direct - top-20 influences 0.9699248120300751\n"
+      "Pearson Correlation EK-FAC vs direct - top-20 influences 0.9872023898446971\n",
+      "Spearman Correlation EK-FAC vs direct - top-20 influences 0.9759398496240601\n"
      ]
     }
    ],
diff --git a/src/pydvl/influence/torch/__init__.py b/src/pydvl/influence/torch/__init__.py
index 9b2005a20..3bbd9552c 100644
--- a/src/pydvl/influence/torch/__init__.py
+++ b/src/pydvl/influence/torch/__init__.py
@@ -6,3 +6,4 @@
     LissaInfluence,
     NystroemSketchInfluence,
 )
+from .pre_conditioner import JacobiPreConditioner, NystroemPreConditioner
diff --git a/src/pydvl/influence/torch/functional.py b/src/pydvl/influence/torch/functional.py
index ca1c9f553..4c82a1732 100644
--- a/src/pydvl/influence/torch/functional.py
+++ b/src/pydvl/influence/torch/functional.py
@@ -48,8 +48,11 @@
     "create_per_sample_mixed_derivative_function",
     "model_hessian_low_rank",
     "LowRankProductRepresentation",
+    "randomized_nystroem_approximation",
+    "model_hessian_nystroem_approximation",
 ]
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -169,8 +172,10 @@ def create_empirical_loss_function(
     on a given dataset. If we denote the model parameters with \( \theta \),
     the resulting function approximates:
 
-    \[ f(\theta) = \frac{1}{N}\sum_{i=1}^N
-        \operatorname{loss}(y_i, \operatorname{model}(\theta, x_i)) \]
+    \[
+        f(\theta) = \frac{1}{N}\sum_{i=1}^N
+        \operatorname{loss}(y_i, \operatorname{model}(\theta, x_i))
+    \]
 
     for a loss function $\operatorname{loss}$ and a model $\operatorname{model}$
     with model parameters $\theta$, where $N$ is the number of all elements provided
@@ -848,19 +853,31 @@ def randomized_nystroem_approximation(
 ) -> LowRankProductRepresentation:
     r"""
     Given a matrix vector product function (representing a symmetric positive definite
-    matrix \(A\) ), computes a random Nyström low rank approximation of
-    \(A\) in factored form, i.e.
-
-    \[ A_{\text{nys}} = (A \Omega)(\Omega^T A \Omega)^{\Cross}(A \Omega)^T
-    = U \Sigma U^T\]
-
-    :param mat_mat_prod: A callable representing the matrix vector product
-    :param input_dim: dimension of the input for the matrix vector product
-    :param input_type:
-    :param rank: rank of the approximation
-    :param shift_func:
-    :param mat_vec_device: device where the matrix vector product has to be executed
-    :return: object containing, \(U\) and \(\Sigma\)
+    matrix $A$ ), computes a random Nyström low rank approximation of
+    $A$ in factored form, i.e.
+
+    $$ A_{\text{nys}} = (A \Omega)(\Omega^T A \Omega)^{\dagger}(A \Omega)^T
+    = U \Sigma U^T $$
+
+    where $\Omega$ is a standard normal random matrix.
+
+    Args:
+        mat_mat_prod: A callable representing the matrix vector product
+        input_dim: dimension of the input for the matrix vector product
+        input_type: data_type of inputs
+        rank: rank of the approximation
+        shift_func: optional function for computing the stabilizing shift in the
+            construction of the randomized nystroem approximation, defaults to
+
+            $$ \sqrt{\operatorname{\text{input_dim}}} \cdot
+                \varepsilon(\operatorname{\text{input_type}}) \cdot \|A\Omega\|_2,$$
+
+            where $\varepsilon(\operatorname{\text{input_type}})$ is the value of the
+            machine precision corresponding to the data type.
+        mat_vec_device: device where the matrix vector product has to be executed
+
+    Returns:
+        object containing, $U$ and $\Sigma$
     """
 
     if shift_func is None:
@@ -924,14 +941,31 @@ def model_hessian_nystroem_approximation(
     rank: int,
     shift_func: Optional[Callable[[torch.Tensor], torch.Tensor]] = None,
 ) -> LowRankProductRepresentation:
-    """
+    r"""
+    Given a model, loss and a data_loader, computes a random Nyström low rank approximation of
+    the corresponding Hessian matrix in factored form, i.e.
 
-    :param model:
-    :param loss:
-    :param data_loader:
-    :param rank:
-    :param shift_func:
-    :return:
+    $$ H_{\text{nys}} = (H \Omega)(\Omega^T H \Omega)^{+}(H \Omega)^T
+    = U \Sigma U^T $$
+
+    Args:
+        model: A PyTorch model instance. The Hessian will be calculated with respect to
+            this model's parameters.
+        loss : A callable that computes the loss.
+        data_loader: A DataLoader instance that provides the model's training data.
+            Used in calculating the Hessian-vector products.
+        rank: rank of the approximation
+        shift_func: optional function for computing the stabilizing shift in the
+            construction of the randomized nystroem approximation, defaults to
+
+            $$ \sqrt{\operatorname{\text{input_dim}}} \cdot
+                \varepsilon(\operatorname{\text{input_type}}) \cdot \|A\Omega\|_2,$$
+
+            where $\varepsilon(\operatorname{\text{input_type}})$ is the value of the
+            machine precision corresponding to the data type.
+
+    Returns:
+        object containing, $U$ and $\Sigma$
     """
 
     model_hvp = create_hvp_function(
diff --git a/src/pydvl/influence/torch/influence_function_model.py b/src/pydvl/influence/torch/influence_function_model.py
index 3474faa16..b813bb05b 100644
--- a/src/pydvl/influence/torch/influence_function_model.py
+++ b/src/pydvl/influence/torch/influence_function_model.py
@@ -18,7 +18,6 @@
 from pydvl.utils.progress import log_duration
 
 from ..base_influence_function_model import (
-    DataLoaderType,
     InfluenceFunctionModel,
     InfluenceMode,
     NotImplementedLayerRepresentationException,
@@ -35,6 +34,7 @@
     model_hessian_low_rank,
     model_hessian_nystroem_approximation,
 )
+from .pre_conditioner import PreConditioner
 from .util import (
     EkfacRepresentation,
     empirical_cross_entropy_loss_fn,
@@ -120,8 +120,8 @@ def influences(
         Compute the approximation of
 
         \[
-        \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}}, f_{\theta}(x_{\text{test}})),
-            \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle
+        \langle H^{-1}\nabla_{\theta} \ell(y_{\text{test}},
+        f_{\theta}(x_{\text{test}})), \nabla_{\theta} \ell(y, f_{\theta}(x))\rangle
         \]
 
         for the case of up-weighting influence, resp.
@@ -131,7 +131,9 @@ def influences(
             \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle
         \]
 
-        for the perturbation type influence case.
+        for the perturbation type influence case. For all input tensors it is assumed,
+        that the first dimension is the batch dimension (in case, you want to provide
+        a single sample z, call z.unsqueeze(0) if no batch dimension is present).
 
         Args:
             x_test: model input to use in the gradient computations
@@ -240,6 +242,9 @@ def influence_factors(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
         \[ H^{-1}\nabla_{\theta} \ell(y, f_{\theta}(x)) \]
 
         where the gradient is meant to be per sample of the batch $(x, y)$.
+        For all input tensors it is assumed,
+        that the first dimension is the batch dimension (in case, you want to provide
+        a single sample z, call z.unsqueeze(0) if no batch dimension is present).
 
         Args:
             x: model input to use in the gradient computations
@@ -281,7 +286,9 @@ def influences_from_factors(
             \nabla_{x} \nabla_{\theta} \ell(y, f_{\theta}(x)) \rangle \]
 
         for the perturbation type influence case. The gradient is meant to be per sample
-        of the batch $(x, y)$.
+        of the batch $(x, y)$. For all input tensors it is assumed,
+        that the first dimension is the batch dimension (in case, you want to provide
+        a single sample z, call z.unsqueeze(0) if no batch dimension is present).
 
         Args:
              z_test_factors: pre-computed tensor, approximating
@@ -452,6 +459,10 @@ class CgInfluence(TorchInfluenceFunctionModel):
             in memory, which can speed up the hessian vector product computation.
             Set this to False, if you can't afford to keep the full computation graph
             in memory.
+        pre_conditioner: Optional pre-conditioner to improve convergence of conjugate
+            gradient method
+        use_block_cg: If True, use block variant of conjugate gradient method, which
+            solves several right hand sides simultaneously
 
     """
 
@@ -466,8 +477,12 @@ def __init__(
         maxiter: Optional[int] = None,
         progress: bool = False,
         precompute_grad: bool = False,
+        pre_conditioner: Optional[PreConditioner] = None,
+        use_block_cg: bool = False,
     ):
         super().__init__(model, loss)
+        self.use_block_cg = use_block_cg
+        self.pre_conditioner = pre_conditioner
         self.precompute_grad = precompute_grad
         self.progress = progress
         self.maxiter = maxiter
@@ -487,6 +502,25 @@ def is_fitted(self):
 
     def fit(self, data: DataLoader) -> CgInfluence:
         self.train_dataloader = data
+        if self.pre_conditioner is not None:
+
+            hvp = create_hvp_function(
+                self.model,
+                self.loss,
+                self.train_dataloader,
+                precompute_grad=self.precompute_grad,
+            )
+
+            def model_hessian_mat_mat_prod(x: torch.Tensor):
+                return torch.func.vmap(hvp, in_dims=1, randomness="same")(x).t()
+
+            self.pre_conditioner.fit(
+                model_hessian_mat_mat_prod,
+                self.n_parameters,
+                self.model_dtype,
+                self.model_device,
+                self.hessian_regularization,
+            )
         return self
 
     @log_duration
@@ -537,9 +571,13 @@ def influences(
 
     @log_duration
     def _solve_hvp(self, rhs: torch.Tensor) -> torch.Tensor:
+
         if len(self.train_dataloader) == 0:
             raise ValueError("Training dataloader must not be empty.")
 
+        if self.use_block_cg:
+            return self._solve_pbcg(rhs)
+
         hvp = create_hvp_function(
             self.model,
             self.loss,
@@ -550,73 +588,162 @@ def _solve_hvp(self, rhs: torch.Tensor) -> torch.Tensor:
         def reg_hvp(v: torch.Tensor):
             return hvp(v) + self.hessian_regularization * v.type(rhs.dtype)
 
+        y_norm = torch.linalg.norm(rhs, dim=0)
+
+        stopping_val = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2)
+
         batch_cg = torch.zeros_like(rhs)
 
-        for idx, bi in enumerate(
-            tqdm(rhs, disable=not self.progress, desc="Conjugate gradient")
+        for idx, (bi, _tol) in enumerate(
+            tqdm(
+                zip(rhs, stopping_val),
+                disable=not self.progress,
+                desc="Conjugate gradient",
+            )
         ):
-            batch_result = self._solve_cg(
+            batch_result = self._solve_pcg(
                 reg_hvp,
                 bi,
+                tol=_tol,
                 x0=self.x0,
-                rtol=self.rtol,
-                atol=self.atol,
                 maxiter=self.maxiter,
             )
             batch_cg[idx] = batch_result
+
         return batch_cg
 
-    @staticmethod
-    def _solve_cg(
+    def _solve_pcg(
+        self,
         hvp: Callable[[torch.Tensor], torch.Tensor],
         b: torch.Tensor,
         *,
+        tol: float,
         x0: Optional[torch.Tensor] = None,
-        rtol: float = 1e-7,
-        atol: float = 1e-7,
         maxiter: Optional[int] = None,
-    ) -> torch.Tensor:
-        r"""
-        Conjugate gradient solver for the Hessian vector product.
-
-        Args:
-            hvp: A callable Hvp, operating with tensors of size N.
-            b: A vector or matrix, the right hand side of the equation \(Hx = b\).
-            x0: Initial guess for hvp.
-            rtol: Maximum relative tolerance of result.
-            atol: Absolute tolerance of result.
-            maxiter: Maximum number of iterations. If None, defaults to 10*len(b).
-
-        Returns:
-            [torch.nn.Tensor][torch.nn.Tensor] representing the solution of \(Ax=b\).
-        """
+    ):
 
         if x0 is None:
             x0 = torch.clone(b)
         if maxiter is None:
             maxiter = len(b) * 10
 
-        y_norm = torch.sum(torch.matmul(b, b)).item()
-        stopping_val = max([rtol**2 * y_norm, atol**2])
-
         x = x0
-        p = r = (b - hvp(x)).squeeze()
-        gamma = torch.sum(torch.matmul(r, r)).item()
+
+        r0 = b - hvp(x)
+
+        if self.pre_conditioner is not None:
+            p = z0 = self.pre_conditioner.solve(r0)
+        else:
+            p = z0 = r0
 
         for k in range(maxiter):
-            if gamma < stopping_val:
+            if torch.norm(r0) < tol:
                 break
-            Ap = hvp(p).squeeze()
-            alpha = gamma / torch.sum(torch.matmul(p, Ap)).item()
+            Ap = hvp(p)
+            alpha = torch.dot(r0, z0) / torch.dot(p, Ap)
             x += alpha * p
-            r -= alpha * Ap
-            gamma_ = torch.sum(torch.matmul(r, r)).item()
-            beta = gamma_ / gamma
-            gamma = gamma_
-            p = r + beta * p
+            r = r0 - alpha * Ap
+
+            if self.pre_conditioner is not None:
+                z = self.pre_conditioner.solve(r)
+            else:
+                z = r
+
+            beta = torch.dot(r, z) / torch.dot(r0, z0)
+
+            r0 = r
+            p = z + beta * p
+            z0 = z
 
         return x
 
+    def _solve_pbcg(
+        self,
+        rhs: torch.Tensor,
+    ):
+        hvp = create_hvp_function(
+            self.model,
+            self.loss,
+            self.train_dataloader,
+            precompute_grad=self.precompute_grad,
+        )
+
+        # The block variant of conjugate gradient is known to suffer from breakdown,
+        # due to the possibility of rank deficiency of the iterates of the parameter
+        # matrix P^tAP, which destabilizes the direct solver.
+        # The paper `Randomized Nyström Preconditioning,
+        # Frangella, Zachary and Tropp, Joel A. and Udell, Madeleine,
+        # SIAM J. Matrix Anal. Appl., 2023`
+        # proposes a simple orthogonalization pre-processing. However, we observed, that
+        # this stabilization only worked for double precision. We thus implement
+        # a different stabilization strategy described in
+        # `A breakdown-free block conjugate gradient method, Ji, Hao and Li, Yaohang,
+        # BIT Numerical Mathematics, 2017`
+
+        def mat_mat(x: torch.Tensor):
+            return torch.vmap(
+                lambda u: hvp(u) + self.hessian_regularization * u,
+                in_dims=1,
+                randomness="same",
+            )(x)
+
+        X = torch.clone(rhs.T)
+
+        R = (rhs - mat_mat(X)).T
+        Z = R if self.pre_conditioner is None else self.pre_conditioner.solve(R)
+        P, _, _ = torch.linalg.svd(Z, full_matrices=False)
+        active_indices = torch.as_tensor(list(range(X.shape[-1])), dtype=torch.long)
+
+        maxiter = self.maxiter if self.maxiter is not None else len(rhs) * 10
+        y_norm = torch.linalg.norm(rhs, dim=1)
+        tol = torch.clamp(self.rtol**2 * y_norm, min=self.atol**2)
+
+        # In the case the parameter dimension is smaller than the number of right
+        # hand sides, we do not shrink the indices due to resulting wrong
+        # dimensionality of the svd decomposition. We consider this an edge case, which
+        # does not need optimization
+        shrink_finished_indices = rhs.shape[0] <= rhs.shape[1]
+
+        for k in range(maxiter):
+            Q = mat_mat(P).T
+            p_t_ap = P.T @ Q
+            alpha = torch.linalg.solve(p_t_ap, P.T @ R)
+            X[:, active_indices] += P @ alpha
+            R -= Q @ alpha
+
+            B = torch.linalg.norm(R, dim=0)
+            non_finished_indices = torch.nonzero(B > tol)
+            num_remaining_indices = non_finished_indices.numel()
+            non_finished_indices = non_finished_indices.squeeze()
+
+            if num_remaining_indices == 1:
+                non_finished_indices = non_finished_indices.unsqueeze(-1)
+
+            if num_remaining_indices == 0:
+                break
+
+            # Reduce problem size by removing finished columns from the iteration
+            if shrink_finished_indices:
+                active_indices = active_indices[non_finished_indices]
+                R = R[:, non_finished_indices]
+                P = P[:, non_finished_indices]
+                Q = Q[:, non_finished_indices]
+                p_t_ap = p_t_ap[:, non_finished_indices][non_finished_indices, :]
+                tol = tol[non_finished_indices]
+
+            Z = R if self.pre_conditioner is None else self.pre_conditioner.solve(R)
+            beta = -torch.linalg.solve(p_t_ap, Q.T @ Z)
+            Z_tmp = Z + P @ beta
+
+            if Z_tmp.ndim == 1:
+                Z_tmp = Z_tmp.unsqueeze(-1)
+
+            # Orthogonalization search directions to stabilize the action of
+            # (P^tAP)^{-1}
+            P, _, _ = torch.linalg.svd(Z_tmp, full_matrices=False)
+
+        return X.T
+
 
 class LissaInfluence(TorchInfluenceFunctionModel):
     r"""
diff --git a/src/pydvl/influence/torch/pre_conditioner.py b/src/pydvl/influence/torch/pre_conditioner.py
new file mode 100644
index 000000000..4497d81c2
--- /dev/null
+++ b/src/pydvl/influence/torch/pre_conditioner.py
@@ -0,0 +1,235 @@
+from abc import ABC, abstractmethod
+from typing import Callable, Optional
+
+import torch
+
+from ..base_influence_function_model import NotFittedException
+from .functional import LowRankProductRepresentation, randomized_nystroem_approximation
+
+__all__ = ["JacobiPreConditioner", "NystroemPreConditioner", "PreConditioner"]
+
+
+class PreConditioner(ABC):
+    r"""
+    Abstract base class for implementing pre-conditioners for improving the convergence
+    of CG for systems of the form
+
+    \[ ( A + \lambda \operatorname{I})x = \operatorname{rhs} \]
+
+    i.e. a matrix $M$ such that $M^{-1}(A + \lambda \operatorname{I})$ has a better
+    condition number than $A + \lambda \operatorname{I}$.
+
+    """
+
+    @property
+    @abstractmethod
+    def is_fitted(self):
+        pass
+
+    @abstractmethod
+    def fit(
+        self,
+        mat_mat_prod: Callable[[torch.Tensor], torch.Tensor],
+        size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        regularization: float = 0.0,
+    ):
+        r"""
+        Implement this to fit the pre-conditioner to the matrix represented by the
+        mat_mat_prod
+        Args:
+            mat_mat_prod: a callable that computes the matrix-matrix product
+            size: size of the matrix represented by `mat_mat_prod`
+            dtype: data type of the matrix represented by `mat_mat_prod`
+            device: device of the matrix represented by `mat_mat_prod`
+            regularization: regularization parameter $\lambda$ in the equation
+                $ ( A + \lambda \operatorname{I})x = \operatorname{rhs} $
+        Returns:
+            self
+        """
+        pass
+
+    def solve(self, rhs: torch.Tensor):
+        r"""
+        Solve the equation $M@Z = \operatorname{rhs}$
+        Args:
+            rhs: right hand side of the equation, corresponds to the residuum vector
+                (or matrix) in the conjugate gradient method
+
+        Returns:
+            solution $M^{-1}\operatorname{rhs}$
+
+        """
+        if not self.is_fitted:
+            raise NotFittedException(type(self))
+
+        return self._solve(rhs)
+
+    @abstractmethod
+    def _solve(self, rhs: torch.Tensor):
+        pass
+
+
+class JacobiPreConditioner(PreConditioner):
+    r"""
+    Pre-conditioner for improving the convergence of CG for systems of the form
+
+    $$ ( A + \lambda \operatorname{I})x = \operatorname{rhs} $$
+
+    The JacobiPreConditioner uses the diagonal information of the matrix $A$.
+    The diagonal elements are not computed directly but estimated via Hutchinson's
+    estimator.
+
+    $$ M = \frac{1}{m} \sum_{i=1}^m u_i \odot Au_i + \lambda \operatorname{I} $$
+
+    where $u_i$ are i.i.d. Gaussian random vectors.
+    Works well in the case the matrix $A + \lambda \operatorname{I}$ is diagonal
+    dominant.
+    For more information, see the documentation of
+    [Conjugate Gradient][conjugate-gradient]
+    Args:
+        num_samples_estimator: number of samples to use in computation of
+            Hutchinson's estimator
+    """
+
+    _diag: torch.Tensor
+    _reg: float
+
+    def __init__(self, num_samples_estimator: int = 1):
+        self.num_samples_estimator = num_samples_estimator
+
+    @property
+    def is_fitted(self):
+        return self._diag is not None and self._reg is not None
+
+    def fit(
+        self,
+        mat_mat_prod: Callable[[torch.Tensor], torch.Tensor],
+        size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        regularization: float = 0.0,
+    ):
+        r"""
+        Fits by computing an estimate of the diagonal of the matrix represented by
+        `mat_mat_prod` via Hutchinson's estimator
+
+        Args:
+            mat_mat_prod: a callable representing the matrix-matrix product
+            size: size of the square matrix
+            dtype: needed data type of inputs for the mat_mat_prod
+            device: needed device for inputs of mat_mat_prod
+            regularization: regularization parameter
+                $\lambda$ in $(A+\lambda I)x=b$
+        """
+        random_samples = torch.randn(
+            size, self.num_samples_estimator, device=device, dtype=dtype
+        )
+        diagonal_estimate = torch.sum(
+            torch.mul(random_samples, mat_mat_prod(random_samples)), dim=1
+        )
+        diagonal_estimate /= self.num_samples_estimator
+        self._diag = diagonal_estimate
+        self._reg = regularization
+
+    def _solve(self, rhs: torch.Tensor):
+        inv_diag = 1.0 / (self._diag + self._reg)
+
+        if rhs.ndim == 1:
+            return rhs * inv_diag
+
+        return rhs * inv_diag.unsqueeze(-1)
+
+
+class NystroemPreConditioner(PreConditioner):
+    r"""
+    Pre-conditioner for improving the convergence of CG for systems of the form
+
+    $$ (A + \lambda \operatorname{I})x = \operatorname{rhs} $$
+
+    The NystroemPreConditioner computes a low-rank approximation
+
+    $$ A_{\text{nys}} = (A \Omega)(\Omega^T A \Omega)^{\dagger}(A \Omega)^T
+    = U \Sigma U^T, $$
+
+    where $(\cdot)^{\dagger}$ denotes the [Moore-Penrose inverse](
+    https://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_inverse),
+    and uses the matrix
+
+    $$ M^{-1} = (\lambda + \sigma_{\text{rank}})U(\Sigma+
+        \lambda \operatorname{I})^{-1}U^T+(\operatorname{I} - UU^T) $$
+
+    for pre-conditioning, where \(  \sigma_{\text{rank}} \) is the smallest
+    eigenvalue of the low-rank approximation.
+    """
+
+    _low_rank_approx: LowRankProductRepresentation
+    _regularization: float
+
+    def __init__(self, rank: int):
+        self._rank = rank
+
+    @property
+    def low_rank_approx(self) -> Optional[LowRankProductRepresentation]:
+        return self._low_rank_approx
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def is_fitted(self):
+        return self._low_rank_approx is not None and self._regularization is not None
+
+    def fit(
+        self,
+        mat_mat_prod: Callable[[torch.Tensor], torch.Tensor],
+        size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        regularization: float = 0.0,
+    ):
+        r"""
+        Fits by computing a low-rank approximation of the matrix represented by
+        `mat_mat_prod` via Nystroem approximation
+
+        Args:
+            mat_mat_prod: a callable representing the matrix-matrix product
+            size: size of the square matrix
+            dtype: needed data type of inputs for the mat_mat_prod
+            device: needed device for inputs of mat_mat_prod
+            regularization: regularization parameter
+                $\lambda$  in $(A+\lambda I)x=b$
+        """
+
+        self._low_rank_approx = randomized_nystroem_approximation(
+            mat_mat_prod, size, self._rank, dtype, mat_vec_device=device
+        )
+        self._regularization = regularization
+
+    def _solve(self, rhs: torch.Tensor):
+
+        rhs_is_one_dim = rhs.ndim == 1
+
+        rhs_view = torch.atleast_2d(rhs).t() if rhs_is_one_dim else rhs
+
+        regularized_eigenvalues = (
+            self._low_rank_approx.eigen_vals + self._regularization
+        )
+        lambda_rank = self._low_rank_approx.eigen_vals[-1] + self._regularization
+
+        proj_rhs = self._low_rank_approx.projections.t() @ rhs_view
+
+        inverse_regularized_eigenvalues = lambda_rank / regularized_eigenvalues
+
+        result = self._low_rank_approx.projections @ (
+            proj_rhs * inverse_regularized_eigenvalues.unsqueeze(-1)
+        )
+
+        result += rhs_view - self._low_rank_approx.projections @ proj_rhs
+
+        if rhs_is_one_dim:
+            result = result.squeeze()
+
+        return result
diff --git a/tests/influence/test_influence_calculator.py b/tests/influence/test_influence_calculator.py
index fae2f3632..854321f8f 100644
--- a/tests/influence/test_influence_calculator.py
+++ b/tests/influence/test_influence_calculator.py
@@ -27,6 +27,11 @@
     DirectInfluence,
     EkfacInfluence,
 )
+from pydvl.influence.torch.influence_function_model import NystroemSketchInfluence
+from pydvl.influence.torch.pre_conditioner import (
+    JacobiPreConditioner,
+    NystroemPreConditioner,
+)
 from pydvl.influence.torch.util import (
     NestedTorchCatAggregator,
     TorchCatAggregator,
@@ -69,7 +74,10 @@ def influence_model(model_and_data, test_case, influence_factory):
     "influence_factory",
     [
         lambda model, loss, train_dataLoader, hessian_reg: CgInfluence(
-            model, loss, hessian_reg
+            model,
+            loss,
+            hessian_reg,
+            use_block_cg=True,
         ).fit(train_dataLoader),
         lambda model, loss, train_dataLoader, hessian_reg: DirectInfluence(
             model, loss, hessian_reg
@@ -79,8 +87,14 @@ def influence_model(model_and_data, test_case, influence_factory):
             loss,
             hessian_regularization=hessian_reg,
         ).fit(train_dataLoader),
+        lambda model, loss, train_dataLoader, hessian_reg: NystroemSketchInfluence(
+            model,
+            loss,
+            rank=5,
+            hessian_regularization=hessian_reg,
+        ).fit(train_dataLoader),
     ],
-    ids=["cg", "direct", "arnoldi"],
+    ids=["cg", "direct", "arnoldi", "nystroem-sketch"],
 )
 def test_dask_influence_factors(influence_factory, test_case, model_and_data):
     model, loss, x_train, y_train, x_test, y_test = model_and_data
diff --git a/tests/influence/torch/test_functional.py b/tests/influence/torch/test_functional.py
index 542b5a0e8..5c7b90b50 100644
--- a/tests/influence/torch/test_functional.py
+++ b/tests/influence/torch/test_functional.py
@@ -227,5 +227,5 @@ def mat_vec(x):
     A_approx = torch.matmul(U, U.t() * Sigma.unsqueeze(-1))
     # Verify that the approximation is close to the original A
     assert torch.allclose(
-        A, A_approx, atol=1e-2
+        A, A_approx, atol=1e-5, rtol=1e-3
     ), "The approximation should be close to the original matrix within a tolerance"
diff --git a/tests/influence/torch/test_influence_model.py b/tests/influence/torch/test_influence_model.py
index b4dafcd4e..0631c60fc 100644
--- a/tests/influence/torch/test_influence_model.py
+++ b/tests/influence/torch/test_influence_model.py
@@ -17,6 +17,11 @@
     LissaInfluence,
     NystroemSketchInfluence,
 )
+from pydvl.influence.torch.pre_conditioner import (
+    JacobiPreConditioner,
+    NystroemPreConditioner,
+    PreConditioner,
+)
 from tests.influence.torch.conftest import minimal_training
 
 torch = pytest.importorskip("torch")
@@ -317,8 +322,18 @@ def direct_factors(
             ).fit(train_dataLoader),
             1e-4,
         ],
+        [
+            lambda model, loss, train_dataLoader, hessian_reg: CgInfluence(
+                model,
+                loss,
+                hessian_regularization=hessian_reg,
+                pre_conditioner=NystroemPreConditioner(10),
+                use_block_cg=True,
+            ).fit(train_dataLoader),
+            1e-4,
+        ],
     ],
-    ids=["cg", "lissa", "direct"],
+    ids=["cg", "lissa", "direct", "block-cg"],
 )
 def test_influence_linear_model(
     influence_factory: Callable,
@@ -394,9 +409,6 @@ def upper_quantile_equivalence(
 @parametrize(
     "influence_factory",
     [
-        lambda model, loss, train_dataLoader, hessian_reg: CgInfluence(
-            model, loss, hessian_regularization=hessian_reg
-        ).fit(train_dataLoader),
         lambda model, loss, train_dataLoader, hessian_reg: LissaInfluence(
             model,
             loss,
@@ -405,9 +417,9 @@ def upper_quantile_equivalence(
             scale=10000,
         ).fit(train_dataLoader),
     ],
-    ids=["cg", "lissa"],
+    ids=["lissa"],
 )
-def test_influences_nn(
+def test_influences_lissa(
     test_case: TestCase,
     model_and_data: Tuple[
         torch.nn.Module,
@@ -618,3 +630,78 @@ def test_influences_ekfac(
         assert np.allclose(ekfac_influence_values, accumulated_inf_by_layer)
         check_influence_correlations(direct_influences, ekfac_influence_values)
         check_influence_correlations(direct_sym_influences, ekfac_self_influence)
+
+
+@pytest.mark.torch
+@pytest.mark.parametrize("use_block_cg", [True, False])
+@pytest.mark.parametrize(
+    "pre_conditioner",
+    [
+        JacobiPreConditioner(),
+        NystroemPreConditioner(rank=5),
+        None,
+    ],
+)
+def test_influences_cg(
+    test_case: TestCase,
+    model_and_data: Tuple[
+        torch.nn.Module,
+        Callable[[torch.Tensor, torch.Tensor], torch.Tensor],
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+        torch.Tensor,
+    ],
+    direct_influences,
+    direct_factors,
+    use_block_cg: bool,
+    pre_conditioner: PreConditioner,
+):
+    model, loss, x_train, y_train, x_test, y_test = model_and_data
+
+    train_dataloader = DataLoader(
+        TensorDataset(x_train, y_train), batch_size=test_case.batch_size
+    )
+    influence_model = CgInfluence(
+        model,
+        loss,
+        test_case.hessian_reg,
+        maxiter=5,
+        pre_conditioner=pre_conditioner,
+        use_block_cg=use_block_cg,
+    )
+    influence_model = influence_model.fit(train_dataloader)
+
+    approx_influences = influence_model.influences(
+        x_test, y_test, x_train, y_train, mode=test_case.mode
+    ).numpy()
+
+    assert not np.any(np.isnan(approx_influences))
+
+    assert np.allclose(approx_influences, direct_influences, atol=1e-6, rtol=1e-4)
+
+    if test_case.mode == InfluenceMode.Up:
+        assert approx_influences.shape == (
+            test_case.test_data_len,
+            test_case.train_data_len,
+        )
+
+    if test_case.mode == InfluenceMode.Perturbation:
+        assert approx_influences.shape == (
+            test_case.test_data_len,
+            test_case.train_data_len,
+            *test_case.input_dim,
+        )
+
+    # check that influences are not all constant
+    assert not np.all(approx_influences == approx_influences.item(0))
+
+    assert np.allclose(approx_influences, direct_influences, atol=1e-6, rtol=1e-4)
+
+    # check that block variant returns the correct vector, if only one right hand side
+    # is provided
+    if use_block_cg:
+        single_influence = influence_model.influence_factors(
+            x_train[0].unsqueeze(0), y_train[0].unsqueeze(0)
+        ).numpy()
+        assert np.allclose(single_influence, direct_factors[0], atol=1e-6, rtol=1e-4)
diff --git a/tests/influence/torch/test_pre_conditioner.py b/tests/influence/torch/test_pre_conditioner.py
new file mode 100644
index 000000000..8aa05b863
--- /dev/null
+++ b/tests/influence/torch/test_pre_conditioner.py
@@ -0,0 +1,87 @@
+import pytest
+import torch
+
+from pydvl.influence.torch.pre_conditioner import (
+    JacobiPreConditioner,
+    NystroemPreConditioner,
+)
+
+
+def high_cond_diagonal_dominant_matrix(size, high_value=1e5, low_value=1e-2):
+    """Generates a diagonal dominant matrix with a high condition number."""
+    A = torch.randn(size, size) * low_value  # Small off-diagonal elements
+    for i in range(size):
+        A[i, i] = high_value if i % 2 == 0 else low_value
+
+    return A.T @ A
+
+
+def approx_low_rank_matrix(size, rank):
+    """Generates an approximately low-rank matrix."""
+    U = torch.randn(size, rank)
+    return U @ U.T + 1e-1 * torch.eye(size)
+
+
+@pytest.fixture
+def high_cond_mat():
+    size = 100  # Example size
+    return high_cond_diagonal_dominant_matrix(size)
+
+
+@pytest.fixture
+def low_rank_mat():
+    size = 1000  # Example size
+    rank = 50
+    return approx_low_rank_matrix(size, rank)
+
+
+@pytest.mark.parametrize("num_samples_estimator", [1, 3, 5])
+def test_jacobi_preconditioner_condition_number(high_cond_mat, num_samples_estimator):
+    preconditioner = JacobiPreConditioner(num_samples_estimator=num_samples_estimator)
+    size = high_cond_mat.shape[0]
+    regularization = 0.1
+
+    # Original matrix and its condition number
+    A = high_cond_mat
+    original_cond_number = torch.linalg.cond(A + regularization * torch.eye(size))
+
+    preconditioner.fit(
+        lambda x: A @ x, size, high_cond_mat.dtype, high_cond_mat.device, regularization
+    )
+    assert preconditioner.is_fitted
+
+    preconditioned_A = preconditioner.solve(A + regularization * torch.eye(size))
+    preconditioned_cond_number = torch.linalg.cond(preconditioned_A)
+
+    # Assert that the condition number has decreased
+    assert preconditioned_cond_number < original_cond_number * 10 ** (
+        -0.5 * (num_samples_estimator)
+    )
+
+
+def test_nystroem_preconditioner_condition_number(low_rank_mat):
+    preconditioner = NystroemPreConditioner(60)
+    size = low_rank_mat.shape[0]
+    regularization = 1e-2
+
+    # Original matrix and its condition number
+    original_cond_number = torch.linalg.cond(
+        low_rank_mat + regularization * torch.eye(size)
+    )
+
+    preconditioner.fit(
+        lambda x: low_rank_mat @ x,
+        low_rank_mat.shape[0],
+        low_rank_mat.dtype,
+        low_rank_mat.device,
+        regularization,
+    )
+    assert preconditioner.is_fitted
+
+    preconditioned_A = preconditioner.solve(
+        low_rank_mat + regularization * torch.eye(size)
+    )
+    preconditioned_cond_number = torch.linalg.cond(preconditioned_A)
+
+    # Assert that the condition number has decreased
+    assert preconditioned_cond_number < original_cond_number * 1e-1