From 9a9c49bcfd946e04ab5a8163ea6afba068ef2de2 Mon Sep 17 00:00:00 2001
From: "Claudio Maggioni (maggicl)" <maggicl@kolabnow.ch>
Date: Tue, 4 May 2021 11:45:05 +0200
Subject: [PATCH] hw1: wip

---
 .../deliverable/linear_regression.pickle      | Bin 0 -> 601 bytes
 .../nonlinear_model/keras_metadata.pb         |  12 ++++
 .../nonlinear_model/saved_model.pb            | Bin 0 -> 189466 bytes
 .../variables/variables.data-00000-of-00001   | Bin 0 -> 19722 bytes
 .../nonlinear_model/variables/variables.index | Bin 0 -> 2850 bytes
 .../nonlinear_model_normalizers.pickle        | Bin 0 -> 278 bytes
 assignment_1/deliverable/run_model.py         |  54 ++++++++++++++++--
 assignment_1/src/{t1.py => build_models.py}   |  43 +++++++-------
 8 files changed, 83 insertions(+), 26 deletions(-)
 create mode 100644 assignment_1/deliverable/linear_regression.pickle
 create mode 100644 assignment_1/deliverable/nonlinear_model/keras_metadata.pb
 create mode 100644 assignment_1/deliverable/nonlinear_model/saved_model.pb
 create mode 100644 assignment_1/deliverable/nonlinear_model/variables/variables.data-00000-of-00001
 create mode 100644 assignment_1/deliverable/nonlinear_model/variables/variables.index
 create mode 100644 assignment_1/deliverable/nonlinear_model_normalizers.pickle
 mode change 100644 => 100755 assignment_1/deliverable/run_model.py
 rename assignment_1/src/{t1.py => build_models.py} (73%)

diff --git a/assignment_1/deliverable/linear_regression.pickle b/assignment_1/deliverable/linear_regression.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..ec34b3432694a0e39acd6d6ba3cce55f5fd6caaa
GIT binary patch
literal 601
zcmYLH&1(}u6i-?=CIw5Ymb4xU-dYNat*v^Hy4r)#)QAP~<1)_f&Ss|BomppgQz8m_
zuqHAG!50W3Merj23nCE|JbLsXs7Egz)PuL`WYg+9%)IyJ_ul;8$K8FMbNO5*`iQEB
z1WRs*jGG%p!Y84PT&xJ_v9-*(PHK`U#RP}PFnbT~z>Gc+ummxl5b2RPfwnGjAtM~J
zTf`UzPsB}h6<WH$(S~ppRCO^HiY2Vke;?&2AUH`)T9X7p#nP51NPs{eGVqYOHcumy
zB4(c92YS5PH_u9mn^z=`V<P`kDXP@<LaY>MD~kfXpZmCvH6U#%ieqBFxNmeJeG{tl
zD@ON|rt#B*dz!%x1*f3hdI0M%QyJFz(@?EeSN|Bvj!G^8SC8N@6q_iDa15=~{rdLC
z>)Vqh=fZJ0{9)*Gk36}4@zvJ1-W&hdr}obCGcUSxC(GXVUMC%NJf};@l+FB<7{U`7
zVcQdu*ijlLY(QHVl!rsyFOp8L6o1#}p6Zv|4s01_+LD;pv!0a{<F(Y_5YW-VVCXnK
zOegxq(lVW-Q*@dhH7w_M-d);!-*BFlyFaX-Yff-e)UIu>IuDn+%O4*tI44@OUzRUV
mIi00FmLva$i{SjOGpEPU?!KS~k=aJ*!ZCYc(VmCYwf_RnUhV7v

literal 0
HcmV?d00001

diff --git a/assignment_1/deliverable/nonlinear_model/keras_metadata.pb b/assignment_1/deliverable/nonlinear_model/keras_metadata.pb
new file mode 100644
index 0000000..2e13065
--- /dev/null
+++ b/assignment_1/deliverable/nonlinear_model/keras_metadata.pb
@@ -0,0 +1,12 @@
+
+�Froot"_tf_keras_sequential*�F{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 2]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 30, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 5, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 3, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 2, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}, {"class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 25, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2}}, "shared_object_id": 26}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 2]}, "is_graph_network": true, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [15, 2]}, "float32", "dense_input"]}, "keras_version": "2.5.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 2]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "dense_input"}, "shared_object_id": 0}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 30, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3}, {"class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6}, {"class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 9}, {"class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 12}, {"class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 5, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 13}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 15}, {"class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 3, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 16}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 17}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 18}, {"class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 2, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 19}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 20}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 21}, {"class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24}]}}, "training_config": {"loss": "mse", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "mse", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 27}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "RMSprop", "config": {"name": "RMSprop", "learning_rate": 0.0010000000474974513, "decay": 0.0, "rho": 0.8999999761581421, "momentum": 0.0, "epsilon": 1e-07, "centered": false}}}}2


+�
root.layer_with_weights-0"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 30, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2}}, "shared_object_id": 26}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 2]}}2


+�root.layer_with_weights-1"_tf_keras_layer*�{"name": "dense_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_1", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 30}}, "shared_object_id": 28}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 30]}}2


+�root.layer_with_weights-2"_tf_keras_layer*�{"name": "dense_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_2", "trainable": true, "dtype": "float32", "units": 20, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 29}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 20]}}2


+�root.layer_with_weights-3"_tf_keras_layer*�{"name": "dense_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_3", "trainable": true, "dtype": "float32", "units": 10, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 20}}, "shared_object_id": 30}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 20]}}2


+�root.layer_with_weights-4"_tf_keras_layer*�{"name": "dense_4", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_4", "trainable": true, "dtype": "float32", "units": 5, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 13}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 14}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 15, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 10}}, "shared_object_id": 31}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 10]}}2


+�root.layer_with_weights-5"_tf_keras_layer*�{"name": "dense_5", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_5", "trainable": true, "dtype": "float32", "units": 3, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 16}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 17}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 18, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 5}}, "shared_object_id": 32}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 5]}}2


+�root.layer_with_weights-6"_tf_keras_layer*�{"name": "dense_6", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_6", "trainable": true, "dtype": "float32", "units": 2, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 19}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 20}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 21, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 3}}, "shared_object_id": 33}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 3]}}2


+�root.layer_with_weights-7"_tf_keras_layer*�{"name": "dense_7", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense_7", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 22}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 23}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 24, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 2}}, "shared_object_id": 34}, "build_input_shape": {"class_name": "TensorShape", "items": [15, 2]}}2


+�
qroot.keras_api.metrics.0"_tf_keras_metric*�
{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 35}2


+�
rroot.keras_api.metrics.1"_tf_keras_metric*�
{"class_name": "MeanMetricWrapper", "name": "mse", "dtype": "float32", "config": {"name": "mse", "dtype": "float32", "fn": "mean_squared_error"}, "shared_object_id": 27}2


\ No newline at end of file
diff --git a/assignment_1/deliverable/nonlinear_model/saved_model.pb b/assignment_1/deliverable/nonlinear_model/saved_model.pb
new file mode 100644
index 0000000000000000000000000000000000000000..22064e70ca9d8475a7135d5cd93efe5caf7817cb
GIT binary patch
literal 189466
zcmeIbZIB#Eb{K~0o}TH+!N;I50A>ro06Y5uZ0`coUHv&j>TY+i*j?-wzyWsebRTH-
z&NP5t^mNa4_bhhrxX0s?M;`acBPHI4c>IzelRh44il!|&EQe(&!ltZ{bhI4~`wv5M
zFdU(<L`R4`*?;`w{Iat0qpDt3Re#K2X1B3%H`P@yU%q@V-^-UTUuLEa^PB(U4EcwL
z%%3?#R>|yh-EQMf>&;rHQG2UdzqVx_B<W7Q+urW1*XQTV!zA@it+`!aOl|bu+o~@f
zBJ9DOpg+H6805=jYPC`8KDV)95)N$KG^feLTkv|(xM|Ih$@X?{Yr6+O7S9p*=oDT#
zPLA0}jYM)HHJP5CIe6&sQS;b|lZzM0%tozOtKM#RHfud|vfJx4T6e5TbK>=vK6QEJ
zFgeJc;Queb^eIvysmtwFw`Uy@)wr}Camg_!d-Ybg-SH7mE|K)rje4us=)FftZ?qVR
zUV5hmz2pmI^0iv;we6-kNsJmahW>qvw(uaC+Um47w%45|o+LB9POa75YIp0^nmO@S
zyWO-B7Y;0XKD_1mAeeHH5IcSN7_enJZIU`MU#oZS)Zer(->t9T+iEvjy{>tZ99tJZ
zt6QD=?Z&(H?);Q_f|wiiZm-d*^&0J#`(=J=@f10-QE%3J^=iAhQQc^Cx}tJ}%5aiQ
zyxzXHMP4Hl*K6zdK<bl@)T#I~#_L{=93;l;W)cXwU}TL&$)Cj|WTN(Nqw9RL67vVh
zWioTUSzE8)g<9*Kpn$xcX>pFEx_4_^b(8(ECRP&j26>hoUaQwObUoCH?x9!6#9F<%
zZ5|@&+x1#+8@e|Y+ANu=t@j%5IJ}~77EcRVuN);u>_e<?P9HuB-ExMs$@Cg9x7`70
zK!2HJd<U-2l7p;+Ra>=9M-Fpj)@ez#*4n6cn~in));db27+lw$bhN3vI8WRslQbLM
z-UX^uPf@;7sWi#tjoLc{)@Jrw+b5wm=p*Yr;M4SayS34veQ2K0PENZu{adGe^Qf~F
zKY589xpBAF*{E;4&}h~vE2u4btJZa%flt$pd^;QSN$6mtwY^ydCUrKt^GQ&k^li7$
z{1o}e203}7SL@YpZ#S>kI-sCHNqysTt=TkZpp$j(bnWELMhj%`S{8faCYYGTV}!o;
z@)Ek0^U?Pf2)}Jk+}>`j1HdG-_jcosur&$uz@kMCvbUf{?OxmS?G!mwf49EA4V|G%
zMW%kFl6#iSQTiL}YxO(zcVF0UHo<VL-=!RItPFGp=s?UU&XYwD)mE+7t9M!)5qX`E
z8;;$38B`u5GU=3qUPRSVmYrJkYU4>V-K}rcIyIn-<FcSN<l6?B0Tp@)IwZC7l;P6K
zgJGf)@6{Tux~JIUBV>lksJ`KJZ55->m@a}E0;Evh#mC4I5Lf#?6y2!5Lp8uW=yY*r
z&Y!Xp<N&a0G1aYi-l=D%?MuZ=*^8a^oNc|spR*V9rHgm68@WohSgUW;vlYYqXAh=H
zj~oQM-K{U(1D4gHPbe+RoFIp*jI*?RZ9%;gB(ZWJGs8!&MApb0<J6Lsw3$dJ;C~C5
zGv3A?Ha)eFO>$1Hb%|Of=l>Mb<TF*42Sa8dKpI$kg#eXA(0;<Iqr>O`O@z^p31o6*
z!fD<qZ}SeC3Vs-97G!O)nx>S_Sb>J639r`EsCUU>r<!VRWU^a;#?58UdK))u`s#tf
zd(m5StVa5<gh-H);c4ua+fAXdqmkj9u8X=gkcQhu-QTgA$hxRoB1DpN9t|QwlJ>|X
zITtEPUXPR{e-o>jt|WPx5J@sJa7o%bE=g8{lqCBdtBI~8S&0E8nIEYniB>0hJyMeV
zO{`|RlH_F~BuTv8R_J7YM3N+0on$pgNwVLun&?WBl?aifuW>>r3nP;x0i8@~CCTfN
zlH_k<HPe+OFB2k3Mh32vg%L?IrIjSBK}wSSj@3k0lB`6CBvamQDkNDPnIuyn$)r}2
zydEh@{w7v4T}kpXA(CWd;F2tkNRmmdBv}nolI(Y^Cc2VjB|;>b)JU>4GD#*uk_oLO
zc|B5+{7tN8x{~B&LL|w^z$IB4kt7pZNwONGB-!s+O>`y6N`y!<p^;>HWRj%w4nr$R
zUXPR{e-o>jt|WPx5J@sJa7mU&BuPUnNmhfDB>Nq!iLNABi4aK|UP(5|!L`?Jz_fmA
zsR6U@z9(1H6djvf(V1m%-l$@Ssnt34cf5wFCE`qJr%8((;)OQq>$Ug#Zk59%*{TKa
zjOvK#MNjUKM?Lk$YL^#BoQBZ=^Ko8LvtH}8AQ)VQ07re;20rO+;7Jp&hTJDlq4mdV
zFpe``!+T_wSG?KYgh2K7=CEyk)Z1p$4AcN&j>rAA#%gH*Q@lpLO=fs?oxAN}+jrXA
zzC)&poUD_xN?oy9rIwG^pj#x>Yxin{M5R1d<%G8>Qzk=8K1CjJ@W&#bm5ELGdV9Oo
z8&(EE!X31faOWW&;SOj#!ry|pI^}pZH!SJ%UY<{z95MMUIpx+7i+o-#b_1N|?nDDz
z#IynKW4s2q!0{UJiw2qJn#ID1EHb;)*&HNviAHCi^2)|CwKbBj8sr%rIk8%=MIGTp
zEqMKmpEbw{FC8=vI3QC3@!a!XhRmCqy2$$^gHsWU7aFV~-j<IGzhV%}Q$NHSN7+4C
z<n?JUH%^-&b&_8&$R#~VvDgxVKBSW%QR1)pb%zIU2h$y2H<`;s=#5ev`Dwr2C?8`n
z#j7_;>5$&yiz^3v3lG^2s5d@i!lJuTy+y4P=`HH#Sd8)Y7PWjxZ$To_!QSEqh<B;C
z$l8$JB0t7rim$iG(!=x?9u^*5Zy}ke-lEot^cMAVEXMeHi&{RSxA+3W!QSEqp-0kN
zNTh9%wIRJlevHKwUvH76Lwbvk9YNc|L(ik>Ei6hM)mzj$k=~+yj>Q;XZ&AyK^cEzd
z9_%e{pnD{}B^B9QWNk=qkso6*#n)S8>5$&yi@^tb3lDFPrnj(2cvNpu>qL5s`Z*S3
ze7!|2AJSWpsC}@vxWV+1^p<2~Z;`bjy+wYE#S~v}k)=a=i!aU}>@7TmKAPUbqU}+=
zMXeL*E$Zi3jPdmrwR}i#K~@HWy~PdKkEFLGB72Lh4e2fNV=SildW$R_(p!9M3BlgN
z!}O!+Ei9rR)mzj$k=~+yj>Q;XZ&AyK^cH0GA=q2oMS+p@79+B^$l8$JB0t7rim$iG
z(jmRYw_b6OrRPGzGfmR{ub*=>o=ukcym)AdW!SA;zW&BaHcrH5D)S$o<;l_i?%CCU
z`)qp3Jh1wIJWGBrN#@grIoGVcSMOBsH+pxg_v?*2cYEE7Suz7(rkpSEV;+*`Ft8ke
zB{*0Pz>+AK4PZ$QW&_v+3YG`32@aMAuoMbb0I(DXD*)Ie3RVQLNe)&7uqhO*1YlDf
ztOQ_b6s!zjX%1E<htkvLbbG7U*lc{U-XV{qiFv$Jzq8$h)cP;7eC%es+pTxW$@GkQ
z3^K$aO&e0~tM9nE_gz47(41C(9x|sPQc~+yYg-L+IDOb8G+CdeFq6B4q>q@BER;gh
z=~>9>hVT;9d{kevsjK;zx8^yuW?Iv6rKS_2CRjAlwQ*io(<9!REPqX>l$uV9n$GBJ
zTF}*W)?3p#e@%}nH7$yo9@E$KxUQxryfr=Puj#x}(*;q}Q~H`Rx|*K$*7S_Ori)5V
zmqblV`kJ!3nsVNnY=2F8rKW<Ysi?21q^qgyt*PR#X<4ahMbz|T`kH=RSJPkf*7OOr
zrvJ|XCHkZ}L6?NdboyB{<*cKUqv_|&L!OmKaxlGWrumv2nMz+aldyPYO@lrrVThXl
zNqC%PkMG0darXFScs#)#f65~1Q|$3820Wi;&p%C{A7#%U(C0<={4@0VJbV6G`h0;s
zf0aI8WY0fGpD(fJpQq1x_WU*aTwu??K%bY{^M~|#g*|`WAP2}hBn9*vS@<(+8aeoL
z+%#<XbHX(8@aL3i6yVQk(<s89M@^#ye-=%n41dm>2JFKDvW#Ui2^1xAS@?Si{^sCs
z9{$?!w*Y_h@OK&h7U1s+{4GATdjB(noJv1$o@~KR8O$2bq_3D$n{~*-U#CV@YtGN6
zUocOx0TZ`{N7FAt2XaPKjxO#HK+-RnlTOhtNa)k%o8<7D&#hg3ZuQkG)i<Ae^^Ggu
z9CfS2s__ooJlcNgxtllFuCBgu^U94SFBWpsTnk`U61=Sfiq~1q2*G-Qz^(vKpYs29
zWN<Z11~02J&<<>8)2~Fy;MJfE;thr*{hE-$XUtCr_~ho<@I?(&2K?hv2)EueZw7F?
z2`(&w9F+q56o!{c0d$#aHt2HBk&61;k;3a?Qn;o{K|AQ5O<#|a!cPRH5YJH{>9sgg
z;E{Dy3jAXjw?d@A0@SD!+}L%9beR;uhEgej4P{bLe>+mR5hjJ3suZ*i(b@DHQBrs_
zD1~@V6iI(7jud#r8kGY7IG_|*;1`vG8xxL{0*qZ$3NUstDX704DSSFi3V&Udg4S6)
zoBm9c6h0f2LOchPq(2u&3Oo{wN`ZeIPzo%dib}zawMI$-d<H57@EMpC)ZdO2J|8B9
zTdEYaj`Z1dHA)J9BPfM<&NoTd;z)r<SWzkPj{{191u9V~xG~H~DO_YyxWuKP{&uAB
zR+tplRVipE8E4ZQQBtS}r4Vmo0vku;NP$N#Q7Q0`14@Af08uHpvB5|w<e3x-Tng%M
zM+$esq;OZ2f_4ISHr<Gl!rMV9#GAB{^u0Jz;1NMo3jE`MQec5KR0?j)Em8{0ObRPp
z3hHl13e7MnY^qYwPEOCJTTxPI2c-~iLQB$HLJF^#SA*Up&v~I!T<a+jta7rT!`S|s
z`APtpH<j1QNNmouISk$B&CdqV;fb=AEzx?W;9=-*m_HFfAIMI%E=tUNCLf0Ms`+97
zshS*WU6A-777&Jtxq@F{ovlMV+X48G&YunTE<xX=AU^6$zbDxAJH@8olWh7u#irkB
zI{l`lNBLg?q90wJZaSUrnJFh35W<t&-tfsgYWU=ND0N?!dY6|voQBgis<&I!d-v(a
zeZcZXFP1N<Sgx6`1=!4zSgnExg)X_%=%wruON9;5Y!so>9vq?bDNK5F3fpCJ>QOlz
z@$=;;%xi%r@O)6L&RldOP?T<lCXO<)8LSiDXAOCmH^d{FpE1m@7=fmE*E4u*0is^k
zO*BZr;?ql)u3f)*^|h;?xw2Nh@#?jkONzE^cEkAnW%DNkG-z@Xtu>Sa7N!`q{;wM5
z2SG0Qmb%3JUdIOkFfbp!VwhhJFv2SgCE*Tb1&<Z%C@aJyTgM6ku%oPy*<>gy_=J5&
zSs`Y+I#vjP9c6_~l|os;XU#jx3NhW(u|fdsC@W+h5XuTZb=^@`h&i8*6#`&KSs{~L
zP*(8y<BqaIOuTfg5CA*M3Yo!yvVu?Ic9a!j_M>Bk0N7Dh$n*r16?`T&C@a29PSNvz
z=oY?>`t90wvj>0SB%i8typgFM7nV66I8+7>j9WQs&ZK|D{~69sXm#Ui<}}}#iGh-T
zI-x&VN_XUx6-Ki6gda;?Gx4trcw_nQS3VfJc&cuU+5O7L2maNKeY#({$>d+%n4J5S
zTM_=%jTO0Hxnl9JZVbcy%KIAs%ES)5b&l6!=l0F{{$&oi=WL}&(l+RJ-;>q#c9Whc
z)_A90-K@20cOa*vvC%c*=%={SH8M7_svS6LsoHKe--9Q1G$HFSU2~3@)+{@xZ7MxY
z=qZ9nW-}LkXLp@3Pmw8-g#4IUgFd_n4+reT%LgjP2gZTSGxRL5^J`C#$Etw$cD++?
zt=Fp})uMX8Q`_1C+LyCgSg!={z6-~)b*lWtGH<tyasJelXa6wy;0Sp{bKDv12-9{;
z9~D;Q=~Pz45l>c(6O!CoxTlGnfyJ@#o{c3*aV)ME7J`LqKo}O+feFFFH6;v-JNAcQ
z;Tjc&#T_<7uyD-_!{UxOAy~MEhGB7yHHIZ}N4gcgx!a1rE!v8;rPzwvoonfLzFPXd
zrB>L^O)G3yq}A6hLaUEmc2=L;&wP|{^#fl4?EpUPLKSuJ-gdf!5Y0r5gI%bo4&H}d
zPDkQk7pfZ*yY%qmU>7PK6T5Vp<6sx69uvFtL*rl<CLktu>Dr;#Su$y+#K!m~a?yTz
z%JBW0_Wc$=r@W*8RMhCN8zj$#4))Y<=yPa4Plxu!wR2=a9olKW&pMn;Nyl**)yvre
z`R8f<AZ*7Ugqf4)M#Vnb?x1zhvz*{t575N(gK>CI5Zw*+vVgNCqNY<k*6EFRsli8G
zNH8o==f$QNRm)dOHK3NSloDw2l~N6(zmznB{<>6y=PxCVo4=H5$o!?G(ejs44V1r>
zG)DeXs^RgMl13&|WR~O-dB)C6`6dhcpL^AiWT+u|V(l^VC}v1tT0#$o23u0em(Ab!
zf2PQHrpPx9a$W^yGhMsNg4FA9JT6@pS<V&;6?&eud%~|_K0>CpTlZS+`>px8m4mP-
zM31O-_5#Dn-_!1=Y8F1G;UhiVb&j6r+W`FZEc1DKc=Q89J8$@_!~k<(mpY46W=T3S
zhnb+KNXkwcb8}t^j%p$}mw7yNY=9%ot%Q;2cYJt-iuHxH=gDe>r07ZBojM#I-hx@J
zdpI^6Il@x<tCi)VowFa&GS>PABo~Xr=EakBPBZUYVJ8@`WUcvI8j#EsJ*}J`W*jq6
zGG{{?Kc`{wjO;MxQT|p`o4G)a({{pP&2XqaJxiXR4_$w^XTmv9_pZ>H{2Ck;)7Y$M
zGUO1OjPv7`feXPX&D_d?l>@e|Wsr7+UAdM%0ISb&+eprlx#~8=n5x~a`g-GbgCAX;
zHr*~v{v<>G^(48X%DY?t!ZuYNDo`g+*HO+Wk(YALIq*Vn;T$lg;+5q>rK0FA-QlzV
zYAru6tJ=F@o+XD^$sFxT*`iX`IWYpl7aoAJs{69E?es%iDb1{gD9r#0c<DSXonQ8q
zCbDh^ppa8a6IpkGmM)f*(#{TJS(*V*ws5PJlBJ8ZbUCk-c6OY~(hPtyhL=vt(j{8j
zVJWwk&JILbngLM8@X`rcx(uaDS(S2UN180n0948i#NM89B)Fu})(6H}SHh7Hb|geT
z5~2_ZQH+ErMM9Ky5Y`7t63!2;zFT~xk;+o3Sn|hmc6Y{`g`F91uF~=5`L$K@tZIK)
z*XYCa6e?x=Yy^v~KS+|OPNx^clcC*N3>22fE)~5$%ey10hu|9I`WYvR*@FFOq$sRE
z6ynla0KK>_L|B)Az}&Hd3(OrEKC%Myc->J0JN4y+z_h|p1g15}kpk0V4+zX1p1Hu>
z!FYEBMs~&+hB@E)zBDXfkpj~SLlKyEe2f&B7JEQou1CZL=K4RoBe2x&3#`zWz=}mF
zFs(2Yfoa{9NP%gw2L$Fip<H0D%egxOOYXkFihT*JY)gS@g`o&c>peyaOp84rFxT(r
z0&~6m-4R$~_XSq!OJJ2{DKM=t6oF}-`AC6ju?Ga^PPMqe+=<oh2+Y`hftC9aSShQR
zM6EEa50f&8V~TK&<@$fxa#9ovw?aL8>m2(I(QwUFRzPTd7XhIiECqztItvJ`IS>%q
z{wE-`Z0S3Q1{wL1|Dy|{z2Fa`6-hoAM1#ZT!~e5C5iut;!)N98YUDJ<jG9tP9NPZD
zacBV~4s8$OIJ5u~hqjM#99jU0L)!~E4lRJhq3x#}hZaEM(Dr1GLkrMfAT<#qkotpZ
z@<Rg)ocCe(mb1m8cl9Gn7U0^~&F!X>^~F!aX>W<tFC|FG0(BS3S(@Ts+n^Ep+6G>P
z2mY|o-HBBw4j0%u>*8;Ut8gC}$Ju?azW3HI83g}g`tSdXXi8tFV%aA(wOU<yF)brl
z1kui*4Onq5cZH*(`dvaWY`E9Ytpd*c*6=OBg(&gFJ_*a7*VSpk*~Q`>V*X&wGFn&M
z%XwirWdRrU5FD8n^;+M;7kOsgI^`IA@)k~pb>bE(umJ?MBg0g@wH{}fva>o-nVN26
zU()n%6IZs;8?^5ejX~>oZtGdj?Z=?8HIu#!S_)Z<&~W&n8nobnvwOgxrFPw*c@LT0
zdj>6KD+cY;?#<cG{n=K)j`eMpQZ_8p)4RB>@8G7=q#sw5Yq`N>@3^r{?Tk+!&sY*?
zVSJAnOA^ET`^!Hx$e+%~NryQ?W@xIsxQ^ajB4=t{nqIHCYHa<2Z5H);cnjv!NP(QY
zbL1`X-IRsh1v5_`4Pduj;9NCO16yDRlN%_M7lnLv%%OD@TyQ&B2U~{6>L`jrvFa$f
z;CZ|bVq$faMIm1u1Z_vf1uq2a5W`b|SIeT1ua1;iAd3O;T-Jpz1}kAJ|Ch+QU@0#1
zV%1km60e1ey%em4WeA|PaJiTKwIuLbxZumdTG#{yt%Xaz?61WzGg#Y8Rmk(Dch*(u
z=?&21C*Z)*1E3ng-kvK9Sm!JT2iO4MJb5a})QSN1oUkAYT_TSM3oVO6{O|+;{}iH1
zc2>Z{;RpisG4e<NwVkUvoBG3nY)D9g1SA3U1UVJNlxLW#G-YK1QsoYqC|H5vQouez
zo(W<riYBPGioOK&Jn@93`cz0JSY8T{m8V_UEzKvV3+A)rQm~{dPP$o+TQOhV%#y8O
zv`ZPYx^qsa^VL3w*DgiP%5wYWtDV_6toFPVKF5Hx9j`r~qt!Uwo|Wa652>A)Xzg|Y
z15!Id+n&$+%5t0NtDV_JwDv*(15$emuif9{xUuxr&I~46yT8vd!|9giy)KE@?(cQn
zi27=0h7_&c-|Lt`_0^uhYxnm$Zd`q}GXsm&UhwxiW@vr28y0vcJae{Mu;-_o<k{Kh
z9REa(1)2U=)!YN)Sny>QeT&1|C~O{mTR`6y(YGb^Z5e%ALEkPvFoF$6$+gi2+h~Jr
z90S^58*Q+SHrPfRY@-df(FWUSgHgirXoK@;gY#&E^Eh6#!Fja7d9=ZKw843_!Fja7
zC<z5<aIiA~UBQ<?R`4ZI6?_Rq1z#>(fA0ibna7gp-?W!{us6KjskUmHbvTgdGR;T!
zpLTBrc8NoXa)l<vXVlC1rZRv3I6v|JcaE<<I1VSgJ3y-g9+|s3(2=-kZSrgw?E$@L
zZTvVFeJQMMVlRvS&?K}requ_0wTV42eb(mgj|nM-wN31u2`Puk&D~cMQVOe0?70ak
zht=lp#|bHg)h71pgp|W-bNBItl)`EgdwfF5VYRvYe?m&dKXz`lk2B>~2RJBhwZ9|a
zR%4&=wmQ&JWq7HmgS@a((I<-Gr6TLPiJq@D<OyG^J8){(Dn_ZavjZMqf>O$@@Sr#`
zN~Mh+6f4G4Zovn|iBT%;`=D4cO1bqP6emWh92*A3ic!i(jzMu^l*(~sP^=iGeAF2f
zCq}6plLp0#QOZZGL2+V~%JFPatQe(yv>O!X>FVW|u3UcQ`n9XC-*oPDxVHN8mCHA)
zFRndz{Uy3KguBAlpVKRqg_qpDTidEvX;OQ)*;pUmQ@+C;<y)(t6K?W>)sg>apAKt<
z^_3)<7L7FJqouAUz?mU}59(||uC+fV0sM>-K>kU^KZXMzt85D<-_`j02Kk<`l9=BY
z|I1X!<VNqkE$9o*_G@uYgZrRA$HAUam~P#Xxo%3X;W5F{6~^-JO4iRoky$by;3O;o
zQa-ASZq{!YBoJ~j18=PGWg{!NkJV4sT!0#Sri6Nc7;Nbb)_ac-zC{*JWRh8z2q$v>
zDr0cspo-plV36R<I2Vf>e0_w57_DQHWNk-}!4EiLD;VUmRF2r;9I=Pwh;98Y#t|Fg
zNN_2HuK@9oxxy8D)LgMWT*-%XB|jur^45QXaV3v%C2D0;;YohfJjr`_QV8csVMv}7
z@SajYcoOU>d=-x`Zzx<TjG8M24_AueTqzF8l_K6#iU?PNJ%z7Q+RlQJ!js~tc~bQ7
zq!iAR(vUnU;XS2<@Fdt%{EP4kS4yMiO3A~OayVDYLvp2z_mncim0(ZtW*I0vDUX^b
zWe-m(;XJ7f$&(7+Qz{5gf<48bN}+J2GHR|=JX~20=gRVsTv^6@$}+;0U{CSpiYPo;
z9yL#vtrnS)ZctWX%3RQi%?;wk9`aORecvG1njpfL7)deKoI?9Bynz#2gFal=%N*6O
z#h5dQW9wl~&iWq89L%+i%>7YWlN&i}a$eS`4lc%;K|EX!Yiv!w!F*h#-*6|Fuh6T^
z8OqB=`;F~oj_T)P%o)Vd^)M%|={K0Gi}V|BHkZnpp`2ZmHF+;<RCgC+%^?1+hcyLF
zzrj3Sq~CCtm*-Zf%o)n(MVV9ZGDr1#G3E^7_Ij98)btz7@kRO#_k3B}h{~FwTwjzm
zMK5bq=NDs*&ikFt=%Xzj)|51?!Teu@H8QKnxkySGrH1f-r)`uqB`<4K4;W+3ATF?n
zHD&Af&`}C=f`bm9Kl4)UHbeQrC~wMM-l(21#+yN0VGnOA)_;!j26KkvCz`4}8p<n1
zc~tT8NcD>`9u49cdw8^L{Q=4&%r%acSF5sTC<hs3kJm%CBRph#SP$9GTK_4=9;i4N
zbq&rOylKP=XYA~#eL$d8ojceO?y)_rdu->dzl(APbC2~|(<)C!?iT}%9-c(_#rCj%
zv2AO3g89V=PuwJNl_w*2hk-^9Pa@o5dsuha&Rc&E?I)N!9F`NWa%JS6Fi`2?N`xnD
z59<lr1r1j)PZ;5f*aDz(W#ld}Q0d`HgbQpB>jK+F>%T<%3g!a$wc|l$)5zUnAlt*H
z2zS`lxx+?Gci1jje~hvTbBFub*r76M2*=n!9b+Kc!=wnu*dEp~w#(K(M45y+#(nN#
zQ5iLIFBu5;Fe<`JwukkS?TSX}FfSQVI{&5#xm8DH)5slVAlt*H2uIl-)={>XHT?;5
zl#%}A-yg!4?dbjvl~E&inSpSXQTYg$IX|q+oX_I@2`Ub{%)$N?viVA3Q-0+BGm!0J
zQ-uGVAJ%`)=kWfN$NcADe+u1arZQ^ePBjqjVN`@uogdbz&fA*)ggMnnf6{NKQ`t0f
zKO4yQuqndN&JXKn=kuEWg!$P>e+t_<s4{Bgt~U_wVN`_cogdcq&KETO33I)X{-oa^
zsj_M0zBrKWVN--Jo*&j1&lffQ3G>B~{uH(sRAto2y>TGi!>9;vJU^^Ao-b+o6XuO0
z{YeI=*@j`c<yB?V$o+93+ry>^e>^{|Kc3h4<8b{<&@DjvQ`qial~E)2$ANGUqaytA
z{ILFbUgMAF<N4!xzdtTFF{^AExjznMd)O4=kLQQ=$MYJ0JRi>=&-?vxzNYB&$MYli
z$ANH_QH2P9yfCakUeNgCP;pT4g7zt7Te{Mp3M2Q&f$U{6@p}8(mg)SzKLH5M1g6I|
z(IVFWIi;kz6jWc{lZ=1#mOstJIu>|U%~^BE%Pjg9hqY1IJo>hPzAd6}OX%A&`nH0;
zUB()Wk_!tA2pb^8j=jY(pbdr~BZdKOFa(h>ShT?qAi!YJ2BU<*j1WNp(;w_D+F)>P
zF<7+0;I?3}XoF!$!eG$`gSkPvG)e-f90FRzUY4+zW$a}Id%0|-Zc#n;E491#U@D`z
zZ_`$nE(<y9fA^@lXC^8#(F$?~Y>o9D?G0$>9qW6uer>Bd)_2N<VfCG#yckon?ZwAD
zr`(t_Wv3t`HJLm9Q^?;WH^UDcAMC6BcyNzZ(0Q^iU^|aYWH*wXLGxQpEa?@8&0(-P
z9M;BQZ5%d_!RB$;0tQ>aVT%}S5r-{duq7O}jKP+1*a`+)!C{v%*kx40HX>o1yO1;z
zl(3yeDS-S91QwUDjY-(n(11zU#wBcH61H&(+n9uHT*5XcVH=mQjY-(XC2V67ws8sD
zn1pRy!g)l(ILBa(BPg{zE@24tBe1xH^O%J5xP<eVg!3ADF$w2!3Fk2h=Wz+=F$w2!
z2}1w~;R7yVh=CxmxP&1PfWYDsE+7)dxd5}~Al5<_wG2fBYY~MkA&?~$vW!5MQOF7c
zSwSI}5y<70%W#kFCyB-pCr@autvzI_6KwLqLGtSpB%|IPiQir8Tr3SY%9wDx9gBlH
z*VN8yZkJ}qR%&jSUVy7m#dXj0EKPpcB|C6ZDW%@9I;WOWZ+2w|MJlDd_t7eKsrTU0
zQbfJ~nqJt-PcWpH)Cvme<+z+eb}*w-mwJn9N-d?{d&>?~oF|0ewVhM1>rI-cl`kqk
z*indTIh7#^PdSw%><~k>oXQgH#tQA_-v3~t|0|aH?W#@>!EX4Lmwo5tqb(ov^06Qv
zi}JA~AItKwA|ID6h1be+PI<PK=e+V<$ed;ukVAs2eH1Pr_x{_N>&T6a9eN!(?3%Y`
zjYM)HH93`@CNl>Q9X>MK@8#sL)5~J7uDv*-%c}SI{p6k#A@<(YN`u^8eiLl|^|*JJ
zC&C5+#fFpbOp$LI<h(ivu+N@rDx47j?jiTwMezuka&20ekyNc6cD;nw`utU50PDjq
zeCCu%tFTK~J@1FhG9S@0*7^p1mBKmA^=%?i-kQIq0pZaFBXd4*UJ4l5;KgKn;0A?3
z)bkps=PPm5GtQj|Kb#@o?!!H+EJrKn*faZxcxHbzh-VgJ8>N{XJ7qum(T@z6AB>%{
z-qv;xuFM&oW&a2|Wqn!wIQJlGuMO+t!d|!s5dtiPxC#&R^nR`lV-wH5t_@4<lWW7A
zrg=|jd-(StCU*VWu!NTdd+r{@#7<rtme`Zmh9%S!3`aL#jdyKWaxY#RmW*+2*q>b`
ze=#$BKw~*;S8^VESt5_KS@32*x5v`!!k%E|=%I=6?vPzDE96O5C|8YL9gCJ0$$4Hf
zeuZ<gLOXtiEYK<{WJkEgYoStMzP}2IDJsl6XU4^=kjNGm;#bJRyQ0EE9~Gu#g~j+4
zvdFNgkmVM~YhhAWSc+dE8`(vLEcrBEg$Y?<IevvK7A-1dr=P^D5DQeFb~^jgD(Kd8
z$OX%%*k|<8+Xu#it0~&z>^5__sy-Zn9fiP-K#-3@kdHu6h(b_^Kv0ZAP>euOib7C|
zKv0fCP{t8hzip5(F$Zpoj%CIpZq8otx=Uw5DX_k3kb`PRH2EFZeER*E`qg(E_`p|3
zFW85@)mp!0kWhUIuqxij6>`0{k53EX^;^GYkVA|Wu0osQs_hdhi<AqunKU(hX9(UM
z@lj&EO$Z}_ULk9;TV`FNTw+D!v+wxkf(=JBOYGL~7$n^E1SA#HT?pycj{D&4)@LKA
zuznNHF?YxoCQ@|mdD$G_eY<X_(iqi&MK)*8`iY^3NJiNB03+jiPa&Ot&dbGfQ8ds)
zNRDte20A`tq+qbp^|{PP-TCM2qXBxXE!UO+A&LSB;)Yg%Ibp5b8Wbha44(nnxeSSN
zVMCO_acRBc6H1_=f{s6~5^xBi1QwSna_zLM2E5FutiGZK9JQhbI!`6QN>>dcefj~^
zKu3e31_DIdf48UcHU-r{lo=f-g&GLlQVncG4FuTWBY~m@tOi95G)`bZcCH#kO{^l+
zfN0b}LxrLS9Kx>#tU>r_z{`wEctnl{9JQhbI)6LBN>>dcXPO~u;Nznh4RkapY9K%Y
zYQWnRR0C0Fbet4wAaF}H$RlbXzy?<XB1Z#OgQ5nSsZ2n2t{Oy5_e0dcnf`0kKtqM1
z1{}h#2CPB28t^is5?)HG0Y|N<fo|3qV5O@Dk@1fZHAs2+DAYhlgQ5lkB%lVoO+hsf
zWk$zIp#}oCRD%Md1_EqwHAqP{U^OUepqY>dWap|uR1hje4V<8qSA4=L(omtO0f+Fb
z0c#Mh2E5FugqM_Rz)>q|po@J3Sm~-kWK=Fh4U%3y3N_Htps0ZW38(>YQ&0^=nbC1l
zsDZ#O)u4!|fdCs^4U$p~SPhCAXu?VX*|};E6&eju17xL8sDXwGMGZKFUkzA;a5dm%
zMkTz2R0ED$Q3G8BFThGy4I*R3A!^`TZWL;uqd`#v0TNIH-lm`$h%%$&q)-EaTdF|`
zQ3C-sxEds+8n7A^HP8e{1G00~ASz%Vq6SU?-)qH$8fd6c)PO_y)qph!R|8&VRKhc)
z8gSH#8tCHC0am(d5V?X8q6UVSk3tP}G$?8yKmuyO+Z0p-QD$_U6lx%FTmNAKz17d1
zzzaB`2iBQeELP~okbJ~hr`^x!8jN+8eTATrruc#Y2uC0$@BaEZ0v>wsf9Dj4X^$U(
zBjA%Z|96f+IE;P(j)1$n{_h-t7;60h907L}{NFhOVWj*390516{_h-tkggwqBjBCW
z_r33k`5w<3`|<FF4l{Ha#$E|sPBOz*yX<G9qRr+44@%++V}Bwd>TiZGO-KrK8pSEl
zeU}tyRE|@i`79~WsU)XB_gzw;(OFJ`=Ch<gr_-DQ-FHcWM(H^Pn$MB~ol)Qv=)Ow|
zG**LCp!qB*&{-8uf$qDcKx1||1)9&20-agn6zIN73N&_$Q=s|0GV)x|e}9$y<w(+-
z@~|^Duy=Eu<J8{ntk;#jOXD1;M(wR;y}ecK)VrKHd|T`|$H~hVzAJc~<Md{&<Mf+r
zTO!L!H`YEdTz^6{mI?&Ap;92w421%LZln_kG$WZnpc|+J0?j}q5a`Akfj~2k&<S*y
z*B6+Z?tYHbhq@dm^>_eloah9`6wW&wCpy6du(_=Sur%pFfu8LKu%hy!LjqXiL?@>U
zy5TE!{X<9sZb<lQoanTY?K^UzWO%StDn$u26Iz`Tj1!$$D1)<2iQ?*F3jtQTYA{Z8
zs<*oJs$iR3%ory+t<`HA(xFurGq}g<U(6UMI%N%8EN18&e`PU4fCLsZIE#ae8KR6Y
zlss?(F_CGn0=HCyaiWt}4fwpvuLk2pCshq}(^o|e1V}&)IE#a7Aj*tN4N_7K1a7GY
z<3uN|8t~|WUk%2IPO2K{f-s622#|mpa25yEK$IDk8YHC}2;5Q)#)(c^HQ+HYzZ#4a
zom4f@g%TAt5Fi0H;4BWRfhaR7HAqM`5V)lpj1!%-YQQ78el-{;I;m=)3y3RfAV30Y
zz*!tr15svFYG6n;5V*&QPCo8hJjsVo+Wg;nmXPo({Qw*RcX$2YIRY^v`vEut?kM=b
za|FVc`2jcrZeso4IReoO`~VyQ@0`BxeTNIjiB9;of0^8&%lzZX9lGx_xkI=1Fiv#Z
z;t4Jqm6lS_=`@#u?z@zNM(H^Pn$MB~ol)Qv=)Ow|G**LCp!qB*&{-8uf$qDcKx1||
z1)9&20-agn6zIN73N&_$Q=s|0vNI<U|1USm|2<0<)RTy-y-sbtzESPg-l<oYv*m1o
zrZL@aH0#x^PW^V{-T5i=X>y)^e#fq|^?`f!POIL843^r4^MCKS*$M3}I0x@3@`MNY
zEjUU>1A37><pIrALoxfw;5V(KWcYE*`^HglKW?^u5tHtGe{{3muQ72K`oqn(6vo6|
z><>4;nH6QApWmhaaPwO_W8s!}Rifa2S+X4k8lFgCxQKqM0|`itUcG|?Pn*w>3vP$y
zz#H}T+IuLpVHQZ%2i>gKI;}?QPPJ3()itG-$kV=3o9)edtGB(WDUcyg`U-UJwl%Pi
zlXEU?uidNZR9Hf-x3^n84X!82q6b$l5{_Zh!Ieqg*AB0>+0>blBFUizndQ+ITP8)c
zn6yILqB>H!MbZ}3)hVr$wy3U7ZmF~db#+Rsr7fzflUp!tL0z5FnrVyb>g1M9TToZ0
zv~t>_x;nYV(-zd#DXpKjsIE?K3AF`vbxNzKEvl=NTS#reS#r?TmGyS3+nb-V4inM<
z{p>Y*?^Sd2Q(rTT2Zs4ty>qAjrhWNtef{26yV2@(tzS0C96h$avA$NnQ-Ak`?Pe49
z4Xxib(YKcBd3bNf%;+w~%p50ETeTio?UqT*^sR1wWohYB#y){pY^{^jW!ean{fS%4
z3`uSD-rK61i7dUvFZE8Xxm`C8t<I#U%mb^bOIx*0&z^E|TTL?MR8q|iQ_U$}jk!AO
zR70x0`sUV>Q`fYp!uq;FCU4Z6_4S_V{<Y3vOqYAa4m;Gz-?J{Y;*Fmzkyn$ZISE#e
zkQ==Y40A6-7ciA)k94ggjN-5wUpEu;37y>O8DFv<>obJ3V2teEt#vlKF4J{I)op^w
zjLEq?y*ianng>=4gW{Ihte+ysZ&1ATjTgW%Zq+vHOHMtgY_o&bdccO0lF@ps--Q#c
zd|yoe3+tHVhG43-;48)mm*0=cW{Q_y*=yTFV|YI7)^DC5$8Nw};Z1v~2N^K!PL*<t
z%fpY@3S3tKy=Y|uj)u>uBAUwl{o`ClzjJ)`!Ew+92WYj_ti1=`+WkiFZuNe>ap!KY
zdojDjW)Vx5p1XN-?ds|qH?Q1Sdh@xptIw^zdZqg2bFaQ}Wwn5oqYgt@DXg~KfVJh8
z2C2;+ur_<3+VTU|mLI6L!hp3E2CA(%U~R>LYAX#`TWO%$$^+I`j;!se_ExX4+4y3;
zvqYy!Vak$`pw1*I4Az#go+(mTnTxUvpCE;SUy$H1Jqm*?fcb!sMQy9Gv{{F7Y`wdb
zU2>*NeV1}3JbjkR4OCZdDYCAcsLkWH^4j&AS6{pOnJa768?RowxukGwGt?%op;(!1
z8x$u-skF0$V#O%sR(MdH7^Tui4~i9|lw0sYablE8`#vaEj8bm>2gQj|D#wOFv0{|+
zkz-Jt7^QMt85Ao<DIawP#fecW$D~2AVwCa`YfzjRrE)wQ6e~t4AMFOkdAfS}r7M?T
zxqj{H>o*<O^4jXlS1#YIzPR?>^_Oh8cMb7_tv@s1VmRg>ckkA=>eX5cV(-ny`tUyT
z9quCETK$}GkPobm{6FVfO1ufyKb?hHpz{qTqRwB-@=Xy|=-L9E6u`Ylrx6y3S%`H`
z!zc+6cUi)Uw<KZ3UyiW6RrA1KFdps0DFb~7s23*>>>+?&I%Oaa0rleKfjb1yi&F;b
z5Ku2p9+*P_y*Onc4gvMz<bgK?(2G+B+7M7LP99i80KGV6APoWa;^cv|5J0|Cpj%e3
zVlOFJeJ>|i{VXL|F_#gn$Rz{;p<O<(Vk{j9n2==yYmg-atN-N!Yp|sP4SC#J;NezH
zUD9%Bis7u_Rzy<>Z<n+vnqqie+`4EA;dMz%qbY{h#jTE}5MGzGK$>EBUECUJ3gLB0
z%cLoW*Tt=rrVw72v{;&AcwO9jX$pBl{{GP2Tp)CpAO|uPGU)`#yXGbGjGZxj|E7Jv
z#m^~vL7VF^{3C-r8C*zMS{I87Zejv>OcU2@>-R!EjHz1;tDk%bXVc)a!SlzaLbl}^
z_Jz0<(L76J6~si|SxfO6DZL&2+DJBoio=Fm&4ZO#9rQdo6}xrpR<Inx!kg}5$l+>x
zn_gu}qwiZ(E+nyXK)r6w+9c$vSS~Wt{MkCnpJX$!x}}&-1o|DT3<CBXRW-NxsJc33
z9+O{0HJRT}kVDl*OQHJL66D)J_evV#&(1ZzP9%E4`R#0ScYe<~znybze7}kFn>gQ{
zqpqFbDd)Fy=CAWR>HKyMKXra5oZrq7C(dt!-W2Ei@1au!8i4d9tXr>KhD(}1Ni<>#
zyE0wLIa+x<ZJ4m(F(7M@vS8Q3$~h3zX`)|5SO7f9P&`bm=h73Qc;*04Dijav4(HO7
zk$9#e@epWNIus89dZt71u<my*O+xXc0MAS)9@cHor4NSUNdlfjp?Fw#KbJlniYEbh
zj)dZ2YH}_;8;ZwRIh{Ug!kwFe{^QsR_-clJJr??PF7)g1(61*#zn%>JIv@J=k&v&C
zr>#h?Qf)dF%2ldOr$g~@Z8{T*hicP8C?2j&XCv{Pi^M~<>CsR;RGSt<@o;T=EEEsb
zrpH6^aBX@b6c5#=CqwaYZ8{%{hicP>P&{0lo(jc7wJD?TT||4GvR!3s1M9VB(}{C+
z>b>nwtGeE9Z8Um~b_){Fn(c14-i5@|LUHixl%4)!_H~`|+&;NZ*=d@(7H-fa0sQ3M
zE^+U{6MLlieRyZk0t4^tpWF58lzk`T?78cd=XUZs<+(k1o${RO-|zNy%5&5~wzK5K
zDtb|=sd;Z-(&&zft5kpIjn2imPWgj_<ku%iM$N$Q*1xb#6X~cmbh23-dwIJBMln~)
z2M-l}giLL>?zP(YTgthb&V0o@Nv7RT)hvAE=SnV^r{ss6Qpy>y)l#-nYECVcS4uH+
ztJGD{l`8s55w)(8Qi`Z`m6cM?(5sfJD5X+rsb!^<Gtk12ML0KdDm#)e+{rI0Kb(Qp
zQ%+?FoovW*Do31w)KgAn33ku!Nt&KlzrEeO4rznVU|8R{4CAEr4<=xN2>)j4K3JYJ
z0eSDRWM7bvIr(VI$Gm(j$j735EXl{Re5}aFWlM4<t32nFXIpvBE6;_@X_CANse-U(
zW*;@?eE%|s$iz*EFPJHkv<)M3hMWW!qDiwI8?a|(v(~EJfgG>~Wc|?))cJtZ4`^48
znltGi@qY$P@QsX3ES9m}ZZ&BNKRb?<g#@}LgifqkBaxg)O-`k!$;`n+hmXu=E|P=b
zz1(ixff!V~*EY{U51b;&%h%tSHR!{O@NmFRynLWyd|({NyiSbU=GC<qhrd6)vRuh!
zos~DdZ&=?j(5TKiO<xjm5NrOH28734jMyyQnGxXyf1IXB@<Dmhz)k3Xw%xyC?a(%?
zKgp1PJvpL2VG`ncd9e5D3~?<%Ww~5Z4I&#J9#KA*soj$U#98-x-fFH2Wy{oj@zZzN
zAaTkIpc?NvQ<S!y(rV3Yun?skfQnkTQaVpdSJb*`X*xErTa2p=K&_j|x(iNewQe?e
zh|&(gvRZdamM+rL%WB<h5D}#rfLeD_mM%G^)w<bGB1%($N>;5qAxoE?k80g)KoO-G
zfLb>;rm*d`OB!{4V4P+9X>ySeb|geT5~2_ZQH+ErMM9Ky5Y`7t_u2&=gnl&TvnExS
z_z+8#BbPbM#w*D7x05hl?aT<Zka;|GgqkE1R>Da1J0@MFW76|$tK?bL31wYF@jq!^
zihjOcSuRw{HovDx$71Ubk|e6r>BYcKrq$t=1DI<YT*GB~ewKDeRC7Bcs#mC}URZmc
ztOiAuQzr{i!YXD9_M?%au>MepOKSo2;<^xFT>=7g#|kbmcVzg;3apQ4SY^3r=cK^2
z!cYXJHOP?y(_#+@%pIP&z}&%jcLYXuU&Hc!X;{7@1*R2-A~5av7%4C<_JF`#kBAG*
z^?!CpV5!{~SfMY06^l|}T45*x)4D5>0@Gp-2+VatxxiePb9V%m+<k!+`x02$mIBiX
zLlKzPdyEvA7JEQouHVlE=6d<NBe2Bo3#`<az$(j9U|L})0@FJ4kpk0V4+zYiYH@+N
z6RX`3n6di;EB7O?QdTjET47ipCecWf>;GxXNl~~*dg(lS>zue@QZtnm5L(|wKxhX`
z0im_d0zzvJ1cbK#2?#A)K59`me%ql=y6(Y8y3h7%<TS;Mno>#}+Wx_DXaOV+Z4cr&
zv;Y!^wvTZfS^$Yd+Y31kEr7(K?WY`v7C_?A_GFGj3(((cS|Y}3+IObNHw|(=I6L<E
z?#@zLp;YmNO3_s`7H(8lw$x=Kf}{*+S7iPw(SNkr;(;e*1!s3gsQoa@dPK|Ey|4yC
zc4iGE<6i>-E(F&=*sAy->l}UEs~KmV`_HbDzZl8Anzn6w^mcruCcib3ug|a*mLiFJ
z^NzCaGiO$adjVJ;7E*hg+w4d%d7z(I7gZL7n3WV2vRxk`H(n_f5?Ns(euZouM^wnR
ze#EPAe5L02N=<PaB)`|ncZRUIPEuS`695=rsVSJKIk9vfT&ek0-Ibc$?;78xIlfJk
zILlaI0LHgzZo?M+zQYr9_AEKfmjYSZv2u-|?(A^gHG*uXY{*a_vTe_oGc4{;)6t;Z
zksv?<cO>wm&4YI&h%&yxV~8wkIA4u((#>;~SB0><F3kz2qt5%lM67K9@f{`yIX0gX
zXok;#>|Bp>e48fYBWIhU2I5*sUtbBZGOkZQfEws%P}D$x1k`}HDX0dbjIU{<Py>Nm
zs=@d+P3?_2{E7jw8^q%Mk?QqIji%4SxvX#$DxAdf1pCIzj7oS!js_gHq6RvDTc-w*
zGtCe+Al^+hVl>dvps0ZW38(>YQ&0^=nbC1lj0OU?RD<ztnp!pB_gwhZV0@dVdU2HQ
zIuS(;1V}&)IE#a7Aj*tN4aT==%44Vvk$_p%T&1Git;`|(YB0V{Q&j`qoi>UZ2#|mp
za25yEK$IDk8jNq#MAblZpO2yj9Kx>#<J&Y<HPBtGq^N-a38(>QaZn9JnNg|1_%=;c
z4MIY+ZfJ%>_|;&1o2IG;y4$@JH4q>HHQ+1`s(~moDm572rg?44;x|c(*^!UC<J&ZW
zN#olzYa4I6x7A(SGEX~Ek0tyN0egj<V&BozGt97$@&-X&SeV}+sQWH&5ac0eUw`H|
z2x>k{3PjY*k6?kvgEZ~useDx)?eimWS4B0SB?UT_<UG)QmptHs77YcO&yoV&Jd{(Q
z`z|Tqb6gDtn$MB~-4uXRp!+T<;L{5Y1)9&20-g88DbRhF6mZW@LxJYAq(C?DaSC+b
zB?Wv?)li`MJo>~#?U_e?>_LGukCq2N^N8id^mXP@3Q1tp9F+3Fzz;;Re#y|CpmNw3
zfm;aAC(7s#_j8)&4c9*WGmlcce&$ii%Yr?3=22=V&pb-)$uo~q4>P-y?8P&WNQ^A)
zA54=U8rUA@es`yM5<#;h!onySi|jaszKkVyz`|ZMmQK^Wmg3<XOJmoKrQv14o->w)
zYAnBnpVc$#`r#sb!#a5jcg?L6x6nCB-{~I4!!&5lkv)6u5VxVWop=nIv+bl<h_UU2
znJ3M*6U-2O#I~L6hmDbYd)vt#(BmE7b`nQ3|NT|+mm}GBl833;5q?~jb4olMuQlF#
zGTwVqeG5(;4M|8<HoJ`Xo{aaNh;0*^AnADT$+a!P16}-2Fi{iV+Xs73KGf|!@f_Vc
z-heXRfHK~I;`D(b^2Ns+P~bX3xym^NCq>G!Sf!LH$Kwqsz6FopHu%OTKGyrQYZ%M$
z%zIwOx3V>a@Prz0)Jo>Pc1_s7iZkATlAg1L8VI&YH5hL|(W(JomG`RwV;Qanyv(T7
zV7vikbet4wAo4Y&8jLrfXw`tHhWOQBya7ecwbW(cD7lsbB#>*#Sscu@6lF%G2ICDV
zXv(oB`$);W=Ma817;ivP)j*d9rl^4c38(>QaZn9JnNg|1cmoQm2AUK&MGZKFUk%0^
zP*gS0<svF-AV30Yz*!tr15svFYB1h_f~rACoOZkcMY(YrE<I;=K6|rQ#O8EeKwMD+
z0TNIHE`*>Oh%%#6gYgEG@dlJ;UEzVQhmAL&ctW$|4JfT{9k!R?S#C1zpGPo#_KI(T
z(R`L04@AJikHFmmqx&u?(8Q%U1)9&20^K~6Z+*~xmlW_huD=QHCLGOYNr7$(z$wsu
zmlW{ng@yvnXGww1`{ESnzDo+YXQ!b+^I1}$GfSKT-FHa=A5=9IXg=@IJ%ZX@D4yf5
z!!L(|T`1+jccHMOVEam}#*We6i;2}v)4YD!!%wUxyPjC>J2z?1B~}wPvHH`*IS<c0
z6)%ua-FJF4*^|3a_P`nA`|##%?Og$$TZmZFb~Z-RHoG2FleC>gw%UEflD7B5eNlTm
zX*>BaH(#Wj-DEqSw4I8vKTPY!^n1tU!28446~}$KF>{E9??pGpY1&?MW9D|<jqx1@
zyyx7Qxt(-l=Juo;Gxspf-@;zpOuG=nSbmR?zchxryBTJDxxr_Abg$Zi*_WwY*e9ma
zY1&>hl?%IWDi^#g*mI_GVJA)H!k#ph3lGy&?%Qp;|Jw}t<^*|6y&bOGxYMfjwmbFe
z{Z4IbtKO+DXS2CtaH}7BJsh)rnknCcd5TPLx9+vt_gmGRa`*sqE|pTYvPW!AEtOYF
zv0I&#QUzV9qOTM&l~N_86jAFcE2Y?3GD@k6QYxjET2@N23Gh52qS5MYc}erM@<ruG
z5=M}Osg_e2lJJyMIl^{HtL0Rd^tDa;S0=QV$m#Ez_3YF^uIRI*^yQ;1AM^6DARmkJ
zu_PbM^06Wxmo0_Y%5zS6ww33+@?6NACdr$PmU)~UvyU2czJHlRWa4IfyJx0I(l(6D
z8FI3^-flMQ^pyBIT-DX8-2rWBY;?^8IgmL|4p#wMS;_{q!3*h(j7==KLaeq?ZMT~5
ziOa;Q&W_ryIY&%u)<`5LQj=5ZX)<&0(BUJqnTzD$db@SIai_Y~Y4_UZ8R&siBzgJz
z8?y#|co7~B*ol`9RE!Ud1DU6Yaoc=+ZIPT)HQiP4jp}B5qu#7mmJ6k2dvC`Bc4!m#
z3;w$UizFWum1E_pQ>^>X9wb(9Gkw6kPGa_!1bKqYRkyp)&$?Un^~UW+eWTi{ZPv|0
zrmB2I8!)i@x$d}V=LQZKFzeix7rb8r22RtwF5tso_ZSBZ%$f9$_&)<i&~d<kq#JO6
zWv|hD&pdk7eG*6KWG;~7Fo3nXz0NiaVeJ<E(W}4PGmn^u?p>jS+1fS~-mGW(xm$G~
zu6qn0Fo3bMQXbe;jspfxW>l9<vLl;B;xE5-@C<sC)4&`5#sLGrsT>CkcGpzy+kk;~
ziP*Cc#jcaLV=NH|X8!5~8S@7}VoStxna4w?f0JawN*IZLCw#Ba3EvBA&tpeQdL$K|
z`BYghW(#|KO1KBsNCuhq^>v+moK+9)jL(z25ialLY;lNn4;Jm}YsQBqf$YU4Vy9_)
zZN`_}^>vS=mj!!n-6Oe^>mJEHx$dzCGGzAHj4w_;jn<7Rso^n}=@!S0VF7z(c|dhz
z63F82N6d{$?7kc0kyQAw25wAcuSd2M4|Dx8>7;t>xEqs<k-YJ%Q{-<M<Oy}1g5AN=
zti1<O@AX=<S!L6UI2own%aQ%)<$p*FupY+a7sygr<}h1pnj$GX32RL|v%0jfGkSTI
zPA{HcTOBIN#)+Bj@yW#=$ST-zt&DSMuy%6M$7B+cs`7&;RgLpccQ-~lUQY7INXN@b
zyBj0jxA~`9W7)@^LvUI1-tcKZm$^6_^ZLs`9q&23wl(mA(s<9I-&l_K9PX~MoO5Py
zdn^(&7h}GsHI{vZt-)B9?SWU{$6@Q;^(@C>YrnA^hpl(lSh9_sd(2qI30wbghJ1U1
zWYpkTxBi7~n&MS!1|ruvyW}y7<AnWm+At>|Gw`FBu>VJcB<zO-C}mE4PCkxv^>;j1
z|8*K$y}I@yxe~4BQOh2cTy~EKSU-XZ{e5M23>AFscL~fh8uSxSV9T3*Wp-eTo_jH~
z!)cl~t>NM4@+WpZvm@bU!Jf<PNbF>0M`BNAcI<%_;XURe$5}uAkDn!fa5#FOTW~w;
zb7V>JrP-SEX05lm-E?;6y;JKnYH;n6{=U8i^AjW+KoS2g#0B#*$py;iU}IrSBG?ky
zv&2S_#3y48CBu$S2HW_Bkzx07GbBz%zCSXEiII`-gA9UlqY$4Aws9CEqtFK#DU^(2
zd@@*&3nQc02N_9}j8c3um?MFaQR;(?1WHCZJ{fGEG)6|b4>GX*)aRw{Ejczsb8zbe
z<H-Ody2<5%@suC1Dy|yy9kKbqxPZXMMyoqy7DKB!PZpzAcXBL-R&z)!hE{hREQVHd
z@+*c`cg8D*R&$&yhE{iQD~48cMk|I^cN!~(R&(&G^-l~EvoD(dtIzr!F*6H+P4Nc_
zSxGy`9l)VEUMKWIT)M2^H^@nU&tGEwUhE}Pw?|u-G|)bU8hAl)flj+Mby)wMLE@T;
z0LS}wFfvwv<@`u}AG@E5O_lXO7-T-sI`@QXdC2~w0T3n<<+OdJ1BIH)Ap~LRNwvOi
zkmCU&*Xqq}_juG`3*1J^O|(<QxM?P`M%?or7HpVaqIFt-VvyMOJJ2!u_Z&l%(eWg`
z%9x2yoAu8P@`%5QFlhM{@-aUMC|`5icjukb-&qF{ct)kFbM~naYH3%GKL7`$GJq={
zqOiUb`kwBCGV5O%#PTy-IucT815yzTSJYPNY(groW{gfOr^5)g{w8AbfmB6#v6c+2
zzH^vick9I!SmP-Lx{AWnDw};q&ACDp7&)N=Q(z5k*1y0Mm;;Lu^$Dau#ZqC8X>@%B
zQ(;~UuEIKJHz1dw3P(<3hf`r4Ww;6p=r9%LtO!+MfoXJN!BkjOgR8LTve=*si_(LS
zz_<#tdT|xj%t``XC8)wt@rno)_MI$`sj!ANT!lGshzg@O&jv?fj%ieVg`lG_uLW0O
z-NZ5=m!JwqhJZp;*e4Wh6xLCOtFV9$Q(?}EP!$%KMkf|bg+(>E3VSZP4XUsxJ-7-J
zbQEUw;wr3(00g>9P=%vneIY9B#Qvh)Xbo++3UlBP6-IB(4XQB5G^)OmLRFa8f~&AD
zh!c=YP=zDIry(kw@(Kl0VI5_-3Jd5k73Qo6Rbhc?bYj6&SX6_nFuQTqodpL~Sd<=I
zg;S^svwCqA)<oL^T_vc(QE~DR6-GAxW23N!He7`{aEJ<{H~t1ym}44MUrC}W%xl3_
zSQju2$R()4kxLIDD(pLo08?QdWw;6p=r9%LtO!+MfoXJN!BkjOgR3yREmy0;qV(V@
zoJ3Wa)r+gJCbAvqDnS*FT4M@PVI;!?Q(+BlxC(RN5EVvmD-NnK$26+Gl0a3M*Mh6C
zZgC<Ym!JwqF588uurKEXQ(+xtxC#sCFcs#k2vuQ$X>?-2R9IAlt8fWw7qRHzC@e}3
zuEGgag;~A03Tsx80$nAj!cpt9Au8;w*Lr<6OocVH;VR65LsXdYAu=M(F^#IP7^n*K
zT5uKCEd&PS5>(;HCGQXwHoQW?R9HtDuEGL3OocfsLRDB`8l6}$6&BT4zduRR{E$^s
zJXt}#6-9*JS{H8lW2u_F0qcAa5?$c8G66st?)S08dJ2P$jaJ0uVxZ-bNIxh-t%wu&
zVG(M1bi@yepcQi$KP-Zl&)EE+2wLG!`C$>X+>7*sB4~w;_roG+`N-x6MbL^a=!Zqn
za&7a0_I*?29#8dNi@p49#$CO)ugA?`Fk@z5*l+f+=4ZxVxU_G^U*0uiFH)n_>Z}Dq
ztqv5WR%2NZYBi83wK^k&P^$w)snr-Pgjx+GO0CYeA=K(XQED}I5TRBBiBhXGnFzHy
zP?TDYc}1wzK%&&@EHOf@4iu$UW3>@#HIOK^Is=bTs{=);)eHs*wHnBsx#xQ^mHwBh
z*@M=rWVTy-2hIt*-DuY7y%;N5^VE%AtyjOj-Mn7wz)|>c(0zU5GCf8tx01CU(S5Wl
ziN&+z7{$gfNZG74TGjTJ3HeiG;`R2mt;{Fj1pTTC(r7h$6yyo>G2JJ^%36teBlE`w
zIRbZiywhmifi?fzwe4ol{4IlACTg;pSs-V8KyHG7b4AQbcGj?t`nMD(%pW6DERCRS
zW{Iwucwxh`di7Sf-MQUt--i?}hrp$+PJN@X-dp+Jw-V&fUncXgmvW9B%67leyIZ|q
zZ``@t>t4)~8Tc~ge1RYHaFG!PmIJT^2g?Ci5(TpXEXl!a0GmL;@&Go$!SVo>Lct0E
zmf~Oq0GmX?iU2mr!HNJjg@Tm;Y>I=G04$Ayl>scx!OG-NdfJ?BZ^69^jW5<a<dHNn
zk9X>KwwtiQ;EOfq9)@PS+pTxW$@GkQtk;2FM>if+`3{6Gpg3qwt3MB!)A#C~TDMx;
zYLLU}!zK~8cv0wIG|Y6B9i3Mtr_;Y=m?wF6b9!%8K$EA_-!RN4om2Ld&4pB3;Btr!
zM|<c_V~4=SKYIZ#qv_qQvbyCA#+*nS&?*1h7rwMeb#QUz+@k!r_{_OQ(J1%?w@=jJ
zRsCJK&!*R{I-jUM)H-+M*NxtLTMi!Rk+ojlrbynZ_15pQYm%znyEW%~YrENmPn+A_
z9=%Da*M@7%Zg<+7lzO-=2@u|{HM_LF^=7Txt#a~ih+WhavjEo7?{w}iVEgQse2A{L
z;KHX@=~0C~L>RUH!qsYTdkZ98rDYqv`leF_Ym@!?_u(bRhwfIb)1}PeP3zR|+^KIU
zFL*1NJ#ehX;`@NkU;p!<^mQNIp0e~CB0G$#ZLoxt{CpP5)aVsiPTz(%iya7RQA}`+
zk&|r?@7zuR9jOI3QBf-j8``OUz&AgYFE-n7=hYjn#_e`z(?@N$?lhdTj5cubtp+GX
zIJ(c&JMFF)jZ@U6MX07ji`%~(wCES7rEuR0^gdUm)p9OQ=wEQ@3g~qYnx~=+d>BYd
z)!YDbvd40g!q_D_IcBU+eHzP&+5}f=c1ce80rV+1)~8*UQ(*u(701KVZmUyq05Rpq
zV%lvnl?D)#Jr>h$i>W+-m~sIzH5;vZ&6%@|z491d`RD260w$dh5~;S|dK+f0aPKdS
zcQzePoC%h5KP$ZP&t9Af;tiPVQn4u$O+JTmJsaPI7N*{tayZ?ry<7FYfyk8m#!<m{
z>vZn*rNxUmN|ZC7!Kf`cbENG?bHg`}_EUY+2?yP9X4$lb*xa<yt=@qf!|zsG_1^t<
z=N{DTPU~odOvjI#FtO+Cpp$$Kx7fRV$*t987Q~_0q>wT?F!9z=m;;Hhxw8^FP$$BU
zD8Z40i6R6JyAU1(^o#bT;-xGV(Ob3kd-WEra#6;_=m&ObvLeBceMAA6f^@R%M?TPi
zw>vjP`~k;kQ;hDE2MsUmURVMrFh7W>_gS<8Z+T(l!Y=N0Vbih0KTJdh5Ucwu<beyj
zJaS=I_PVf_2NL%FikslV&e8E}&^WRNr86|_Wka5$Lrwq6y|3ag8rje>Y{cSr28TT@
zZaUcXFYbL6$I*zJ&4315CDNdDhKD^ZZaUobFYbL657Nknj$@<MZrd3j_OiHbI^6Ux
z?tK<l!Ub+SbEW}Tfi)<d5n@jZyzu_#osf^nrBI?2G<AapGd9^u|7N|{X{<YW27!>u
zYxP>|HRt=MAg;3&NNM1y0WK;Y!CF)TEw`Ou$|fMHcE7Oggv;ukPP?;6^$-ab*yZ;>
z|9SYtl8I>Mm7vcJj8Xb|t`W4ueJk>5WTJ<^$FIG1W2@8N@`c@a8<ek22!yt121*Cg
z#-NJqrEE5rW&aB0!g9G#%+ckDjrw}+J@y@bb?&yE=gPA4uUyI%E4hMABdwe5O*o-{
zn+EUz7rdx%bsJ45&XlWP%$DKJIsnumm<S+F7RUSWkNmSVA?YLLB<pJ=ot`x(Xjg`u
zp`-eoAybz#bj+JGG^gea9ZLVQnj`dd`d8v-0sWmRazthUIjJ#j0?^M*$pjiF<VKl~
ztaod2e|SC%*0Hd38Xh`z)BQLvsRa7X2a24?YFdJk`tUPhfCGrGeH;T+z=@Ahbl#19
zzfb-ZwIs(COL9V3l9PH%GOx2Fk9aMK<+UV#(`!loR(wnHyIM=~i+)R@jT-JlTcT}C
zm^FELc|f=|`8Y;E_n<Y&XSIucPLT1fAr||nImu_iwPg0>lwwa#3wv@#Z%-C<_T;SB
zo}BaAlVA1PlW*>fJ<$cG_oF@0wPioqlaFJNeGl3b2yf`7YAlvAq&=}6d-6X~|Kw4{
zo-7J`@|fP9Jg&1RPk8OglU{rBEw4TKc6@vCds_eGmjnKZZtY_~+7n${!tBYz%YViF
zlaJ{-&b~4!;Jg66!@4NUr2Hv0Dd!cFazU7sr}QQzqcbT_dritSUX$`2uSxma@lDG2
zv?k>n0h6LzUfYi*Mc0-vlk(6~wKW#y<GED02Q3QNc8!ygXF;wZos^uM^juUd$|Ye@
zmh=`StFtIMuSK!F7UkEx7UjF~ElNK@6x|B$ezYjMwuD)fhqkLgV^KbytI>PVq7=0T
zg)Jx!X;6wHh>}<ANkQ0?qTZgAboQj|wI>y?J^6L7J^78Du_w9|f&FMtbZrT<Cl77=
zi^iUOJkuTapgqZJ?Fn-thqNboYEPUQ&$41qR)jtIF}*$cah*N+YhHWu39mi*O|L!q
zt@!q&pBaxX2W3Cn6J1-v?8!sir-fUTk7+*59<(UhO<j4m5;vqpfh|+-lW!y->Ex5<
z1Z?-}km>ZZX3EKVAxG2CnTI@SGUQ--)lBn57c!N;Y$hQi#+ruwm1JkLJO7jLILjX2
zhsWdW@yqadf<69}Mbf9(<5vuLKFywgnm#|uo<E?^i|qMl=<|8@{Im4=0(<@{eZI(^
ze~vz1V$VNMpY!bbYxKFmo_~QpFSF+l>GKME{<^Vvl*nUxSV9Xqx41w~KmyD9J?BK8
zW&@6yxzBQ=T!?QQBn53YvhZitG;;9gxCtj8rl6fh9{!v%jRO2RZ5l=R^QdW*;LoCI
zl;O{L)2P6o3#PG5CZQFHTo(Rbg1<TVn}@$P{4K!WJp5gTzXkZa0)LD9m#`ls$f@-6
z=E+vO#SZ`R9d<O6zG6<fX<%WeC7n&bV4iZakL1}ap5v5`reB0!OONK^S!J<LSR(0{
z%t@!{4j!jO&eOA(7X4>0LGGUa+@;fS?9!cj3-&{<S7Ezu$K8VJcEO8eF8$kv=|5O(
z@i;jo&Uu2Xa<^RYg_YBAUYEFnbKbBII=Na)Zra)W$_e;B0atV`z&FCaIcaFo+HeVa
zv!V=<owQ&0Kcao-YFOWSS?xR8thux4SEBmPtHHh#FVl{sUsL+dz-e~SJ0K@-_}+1D
zWx*A<bIBu!^spmRI2_m&xV1u$cB2BH6P*FR_91exc+~!Z#mmJKc|Y)u$X^c=`88GK
z+VrNg>FZG<|B0Z;<E2EA^qL~_f%Bn2<d8fyN|8I)0!oqFp(3~Y5jhNNROB$M#S?iy
z@Q%oDgo*s7DspWG%GvZAQ6hgcDDrq&D<u6XMdSmgtboWNzh%@SCsO43pvdW0X7a1~
zenbww3l%x|F7ZU(54<DtPlt*8ud5>0CJCHPe<n)gpACvUUSa`Be@+qkz*z+#a!5fK
zrO2IY$E3&$L6N7V$P4|592`_Ca&S=NiM$_pN93Om6ZtJw<l05tv*~J-$p1!A<nb1M
zNxG(peBf1I5IHOnk5c5$HC0mN#h}QOQsl*cM1GNp{8C(z_XF>U{H-vNud5>0uCkp?
zZ$yc_9u#@Jl{u2Wt%!W!WjPQztl^DP<j%E5Qskwe$l-)xZrDrxh&<0kUWhC5e&8LE
z-w6}>T~*}Tg^;u9MwH0k4vIY9;t5ILQ$#-S$_a=ZmRUwAa_1TxDe`hq<c1V^xgU`)
zGm)>v6?s4Kj>wx~BHvU+u8mcnO}C;%-VTa9UVNLRw-k{N9NY$xLnM57k)K$ZgHxgp
zTu9mn-HmBWP7Lnk%5j*Q(f5b=dlr#9yK=_;aGZbe#^>0ij+UI|03up<eC3#1c!qz2
z*j<cZ7oDzuf%TkDoSqXMFz`Xp^TvGvwogNy2CFjxb;7SCi?2<v_*#m^*CttfZHmR$
z(hy&ZoQeACMuK}g0D-h{2WlBbTgH@*z6XtYI_9Zo`QW3Z`~Rx}Yero&)ob@^O?d2?
zsrB}Ds|ObnY<t(1-%-|<7k|?rvvNBz3;eTCwe^AF-_%XFsMD>ud>gm3yB&UGi>k^d
z_(*VFI$`mB^7RC?<-TmzyS!P4)9;z2db?G<cOUL9pbG_G^fvQLYBT?PeKUU#ZDwF6
zK5s<G_IsqsA<zSC)+a|-j!@s>T=IfpyOQ?2ViKp0LYEENy*Q4-MPDCT3}5l^)X2J<
z=M^Kur(1~arDWb`WWF1hOjX7aWL{W#5_lz6TI_l5ZK>-mOR!o>VCy<SI%_WW2`>~4
z!#gmUl?yI5Cy;Mj9yk%scVHi1S#<Hu`D%3o`#y|LoY;?%M~kB{vbZsVk@zw4V5AQt
z@zcn{l0J-1l*^Bi$GUtNQ+|vr>gB`e1X27Lc__t)G3m$1f+{|Y&K%v3k<Zk97!!Vs
zY|ie(=u83q82Lodhtcq3WK%*1>i<pHKTMEJ70!LV4F^xO*5Tk)aR*A3t&%z`kzmi3
zv)Q5@AGWd_8#ZT`;=`8m=07pWe`Wk}^1m?1%nNYE$8()K^ypYKM<%GnHJ|!!@<}rJ
z_C&YdyiL+?PdZ!uyM(-*<|m=hubZ{*J@XKmf(vw<QVDXvoFOUrdGEeSf1D&~_cErg
zIrH6Qnk2Rk5c=ULA@}aLTg~^Ja>?%|517ZvVfIRtNPZt)n`EZeYQexq@AK)J^xdqH
z0UYGd4g231<ebWQcZS2SnW`)oO3UUyGRW7VsXy;)>Wte|r}Cq+5zsD&C{IHUD-F@m
zKNp~%0wUyRG7pmH$rB$Y7QL5K1#h03H^`qS?SGRbSJX!0y-T@cD7elI9nZGyk)WyM
zMu8?@$c_Zf^72QaC;zEo|4V~Bp$eI;B>SyLAzLvY806QWJAcd9ozJ-4`S^E}W>9lO
zwRDu7+ttl~XW0MNAkT)jD5}R6a?AUpvH!DS|G*&UgN@Av%zwe&Cj$Hn!~VYvvKrB*
zND=1uy9n)|2n+jNgq}7<im<rfMVJqYu(aPr=xI}=2+R9jgoU68EBjr9o;F2_5FDL-
zG4N~0!eT&##r@*UXvacNo1#Qm-1qKGDJa5y@6LGI6e+@e@6MEiBHZ`xjHgYJA}sEc
zapMQZ>c23^W2yr#bI#qJ5;UHaE1K#R*Yn2WHx2SjFhTqU-vrSU@9+g}j`E-lgnC%a
z-5lklkcMl_-;NWj`I0$%WcK)pQ>PzYJb&ThrF>y|#VR~V=14y=6B;90%zZz3i9923
z?tb*c#De@4h_yhv2OVHXG2!`~nK*vJT6&OthCJ~gxk%1^m^ee+2$~tF7`6fshhPWx
z)Q5?AVq}A0kU&nHNYxkPPc*L$M#%h!iQ^~%8V2<}od5=%IvtxqPRpr8hhdP1Mkf$k
z0J{ea8ibI38PvD44h(v9anuZg<m8YZ<e`x<2qsCupg{=183Y7qdQji4Ffi!+h1d*o
zS{~hlAeA?SK^__zgJ22*3>t(GoIyZ<hCzKhn!uoomtr%>X?Zk*An`SXK^__%22lqe
z7&HhWID>!y4TJi2<A6c=LTm;(Estgpq=|+w$U`Gz5V+64pg{=183Y7q7}U3u1Pp?)
zH|{9rv^<(YFv^B7$U`Gz5Hr$)5P~y^4)cF!6oF}A&ym?tv&ZoT=vvsjaTf!i_Y6V?
z&K#)T{BMj=tjoY+li5LBx$=W#fn0o$%tSavv^%*=g>jrC(XB>AeI+*n>Y)X&NEySm
z6V$_+><AY6U}?>~kMRD-A0(HF{UEs%cj<8u0gR{#5bYXK6Z)6J-!@_|q{Cvq`S!?J
zjVmX$T))}}X_aNOj*vF629AU($Wj;qRf<gYRP`ao9OOfcNQ|*a`{S7N`VS36fR`U6
cpC!o$$pd8h`^ge1cykDzh?P|^z~uP<2VoX@>Hq)$

literal 0
HcmV?d00001

diff --git a/assignment_1/deliverable/nonlinear_model/variables/variables.data-00000-of-00001 b/assignment_1/deliverable/nonlinear_model/variables/variables.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..5432cfe9c617d2d86d0954fe8e563da604fc1b79
GIT binary patch
literal 19722
zcmc(Ec|28J`#zbDlkp%kO)^BrbN1T%oV|{eqLgF^C8-FB43(!Og^YzVR8(kE35g_U
zuOq26Pby84=A=QTH2#j`;jOoL`n;d-^Z6Zrtb6Ts@9VnveXYIMx|eIOl;ErIQr;Yv
z3OCC9Hnnhc864kM%s#V(#w|Ftg3>#vz}=Z(!Oa`L7KWXWC2h)&qE-1L`K?d3LvS^S
z2X2+1d13}I$#ytDNbMwiP7^0%MxW-<)~WD|U8QP*d<T<V%DYh(I}g5J`Va*>4x=pg
zX>kiC?j>JU1tMh_N?w>jgXf2aacVAPLlK8oIBQ}tb<S`z8h*!`w=e?1b(V=RFIoha
zr_3a$8U>@QTc^pV=5+`v5-=z&2YLpoLsn}V5<U2y!=AGif%UUtU_vBXJMAtKUrFN<
z7v8~?GbiEqQ<o4~)j&!tS`DqmOyG{qB5<u;GF#e~k5cbfBhiHbx<6VBm!7C0&CMO*
zgQ09V+u8zFsw5*_S_%4MXaupgN_6X1J*nZm0^R;1ivkDjg5Q?x;wT*CQU_MyXqRsw
zJl(vP=T~C_uPrF0-bEUs$x9cYkK!S4DrQJ|^V`w+jiceSypNpo$FtzeTMDpL(+VY%
z8&Q{@Jd$zN<GF=fqB!F;=oBrE4yT;wfK6WHr&|eV=Af12!GKJte>xkk+gn7MikPCM
z8yTn=HIYh=m*JAgL<pj0pgYHT@YuYgq}$Bhyvv6pA-{YpYBNtqA@~}oQow`LhBorX
z8LlUR#YU9TF&nzPLnL`K2W5^mN6dv))NIAA(1+DXvSB^tZK#1vPAPC2*)jacu>lBO
ziX|KTB+y!yLipY0KAcz=PeR9g)Uj83s32E@pKYE<zTA8WMjLs;hzYBy#XI%j!gr>e
zcD$5KPkBb}?$knct4d+9vMY>FaHfWIWFWaS`{54#?{L(;BS^!`4t7OLqjy^RD0Nr>
zERK9h7Cx6lE}bLDwX8U5?^8**@x2z}D`>!sr#9&F%4Ec76(cVt1F}eNJ@0+=WTfW3
znA|nv0{JXn9^z*Vp>nM}eCXN$3x{d(nPqv<qhcDlZC@py{bmn;Iq`ur&-@C1cEKcW
zuzE4qaZDOF*I)s+>;s!SzPgFbZ*}F?J&#6>>m+Npo^j{Ta-YPlj?P0KXIr@95|8*D
zNo%?4AM?4*sk;2gmK9{)kU`viy6xz!h&0R#sD*dL1K5m?SENDWMYPr|9xcrJNII*t
zQ0X>p^03}?Ua5N_GHwn;Z^q4lUp8CuZC&%A$P^LomB)$j?8q>*#iIaywWLEY!=-S~
zoe)lI#ct|j+;ehzl^*|#R{(OqsD=E$jesR+2uh6Aq?$Jtz?_=FXkyU`ST06~;sHyL
zZ><9gjuYpW-lbq&+7+1e#Tbr{7+rH=Pb@6lUr*ADCP41HzfkBX4{EzzE3C~J%E|V3
zp`spjQX;t)Xy+<1)Ue|t3iMeCwM*5|huxE)k>fs8%g*FIJ2xEN**Oc|J9H41<%poh
zH^-5}AZaRglN4OBPXe8Jcpq}qQpivne<Z(Kha3OyEzfh@a|(%GB)x9ELK7~Y;=J0L
z10C-Az@;VIp$}&`%Dp}j)_FvrRbT|QUMm*vSuX~kPHQC}p4*I;#u-uXCS_4ubK(iG
zwVZsqZXG=SZ5y&SY2|dSt%tMzngTa0|A>}tTTbrh2cy-&PpJnc1;{`<6FzimB?tRU
z!qcl&shyh;Pu#AS%s8A)Rd%_eJp2T?HD)`k+gnJU{JsvIK1-s5o^RoLnh~$#Jwyj~
z$HA7W5Oij}6<@DN4Ys{~M}mZn=u2oU8F{<`j&w|dCzp;!bm^;TYDNOu&pyZ5QY;Ry
zWBR-u$|dM!qaNa3DTfPJw2*sVB~TO3F`!C{9I}4Dns58o96|PeSaYHj@|S!i5AJX0
zb#f}%(RR0LV&v)2;PM*s$O>ghd%PC%(p1s(rV{uCn8Jmgt!U7LM9R0M5;p9<kB;6u
z2gUhM;i2o+sC}*?zv)yOinBL^qO$v7a-up?zL|outK(70$~4&d+Ma93y9d?hLVm+J
zK8f5^Q2dZCSWYXYwt5!w>rY9*H<M1F+Dc>ou8o0kv{n$BIcfu$|8^Y5T1pl<Pgsj~
z*FUFNw-&>wk|Y%H<~}SxvIddw-@zLPZBP)`53aJM!`QcvNEhF;)Ugo~C@roK`sm*$
zN1uMonfz@uda>^Tc_mdI(Z_5;e~qx>LnBwJq1ubTIXfL?ZIgj(xa0Wm&eT!7ZS#;$
z;Wf1Pu?spLGn=~UB+AcHa3M3x3!rs?1IqaPiKohqLAn)H@cZ+*utHA{zM)p2hP!zv
z7q6vwier((qEu9%$m0!ZTu*L&mxM-Y$3ms6#*h}V3)1K1ph4FP(b;xcZh}Dv3e?U(
zyibZS->8Z-U9}BXyp`f!_pyRStYfI!a~t(ec^^_Y&_SOZODRf31+pE>Q2T)vQf5an
z%FsMZy*%ke)^A&l-iOG+ZAs6_B{z3Ko#B;e27dxslEOgyj?be;8Orewjf_T9iezA7
z@dC>I^$POC!znP=<|g7<zajUV3`Y+&*Q3VEdtgPy1D-@#C)JfKhK8+nq;|>fM^P)b
z!MKzhv^6>jNgb0yc2@h*ymfm}=!X<Ir}`^pl9-8F_!{I9g=Nt0t|1IEKE=7*7zr~2
zYDmVuCnSp&OXgdiqH?WeQJBUAXm36Tbv_@0PGCzB{_!%&6A6V{!QZK+_(nuqZ$>`(
zw1K3@jpiSzRe)f`Cb+-3kdilhPpO<e0>3{pMh{-CMtb@k)MklPnCZBby8T`q#_}y-
z%ja6k-8-Io=spE{T&O0CjWE7GcRHGU-41RXasa6;T8PZ@*20x<Uc&HqB8V5A0o~>l
zQ_JP&p)>7KWJlr{K7ebGTvi}zGH@UxFK5G>lUBn+4eMc$ycrzf5e`M-4^a0>8f?8C
z&sX8dp!_TXuF=+n3)?Em4{L6ty4Dsr)#fv0o+pWnoy8#V4Mam2d8AwPG+yPqp)g~-
z7)gb$M%u<}IXlmZaRc);k(N&yIb&HJy2WvV%Zhc-A;GsPmopCKO}9pa$0LZz@u1V)
zDj2Ik=kosYLC2F;la+TQxOW`3LdT6J@Xd-?N<?}i-1W&8rGDRoLevK#Vw(hi0iywN
z)~6uzWjq*?69Cf&k3^GXmm+PMSoo^z4IH;#4T<dafOVlG(A37Oq)z2#c)qEeW7Ren
zB~A=Lk99Snzl9&VanBZ-`AvWrITabiz9d~&xsdUVkKy;*!PKa*WoT~1YE)F63S*4&
z$-pU+uv&d6l&gdsNl6{<>HG`u@Hl-qu00HWH0>ZOcOO9F<`bc2c?!RnUj|bHcc4R@
z7&y=B1X`K6AGy0rz!mp*QRy>|z%->CNL{c(I){hw8>4ois0$L@5odYuXofUYJsE)<
zthP~f*L0Mw-pH{$xE`H9^$pG$TZ$ZAebJHY-^iS+pODoSJudMm4HfLCaXrkg!REbJ
zkxBJ#sI@N*mc<xB|B_5p>lDq493(-0*pz~%s=T6t?03RBwo^zY3nMf|%O2%<=)i9q
zJ2`FpW%=&kRL~8}gV6Pi7K(nl8eN+#cyA5#VC4QMl&4P~<>7t@rF~w5!uKgs3xkcJ
zh~O8qOcrK$c%d?Hny@eWjiT0_-v-weC_ob<PjYFr7*{&Q4VIJ@L8%#OaQ^BGh`)X$
z+On=0waZ45>*lN=3qn(16qBMvjWu~E!8F(|n+F4*o#gC!z83AVnhw(<FOuPfTi`fx
zOY&5JJHP9k6uD;lElSj=8GgC452?A@z(9^R_w+ai)MT6tUj!Ra=91b_Ln#YQx_SY%
z6JmS=y>!x4uM)Mp7o#uJUqg{=iZEpGYBEr960(~vk3d==Tqi5VXE!BL{#s2)UR|A=
z=5q#}-n<Z9NKNFol6%p;u@NZyND_a~kr!x7TRp6p5W|O-#{7yi%i&%XT^NXeB;DuI
z$w?()aO$(QNJ(J?9AP~gHqWVn=T4_n?x9J%a$kM;X;=)B&8ekwPDH|#qw0Lo1w)aK
z=N43PPm-_VaTY#Iw<qD-ENHAtLh%J9f_G39eY*0LGd#*0j#`k6#1!3;WqJ@>VerJ7
zPmTLfhb)f#V%DQsr)R?oGdj%J)(#UA51}_-a^NP925QFJXfjrFDOGYgm;dY$lM`|*
zgJbzZ5j{jPXf|2NzA|SHEc5Jw9lM_Lw$I;25fx+6Ht!car!~2#WWG1tb=8=By73vh
znJ>`(VLa^m;16?Vw4yI5%h5f_^Jw0LWH|X`40Nw-A?X#@Q2H}Z7*2mi`fmS>>TI;Q
zUQ?5)4OLU&=k(POHe9N~qT;C{1!-usFbApxD#Hc0mLflXHI>JPh|f$#XJ+$wiwqH!
ze%%2cp3FrG+65HteF@Z#?xMEWsG(Vx=v>b`zA%3CYFP9<4UOI5gVsj1lGhDnA>-qV
zn(L^MTr05&m1PCPNt4sjM>7%rkQ@6DgDcKW%!TmN<zZYn-kG1{<&B({4(Bh~o=lEj
zvWd@i)#0Yr6_KO&iE`hG%cB$T5PXmw&sCQs`SG@I$gSg&`6f3d(0+wRa;p&wqP?y#
zDl`we9m^!ov|6IcGg3%oUr#;uiA7l*N6`1uEb4m2Q}Vn2c=r3WY$$zk3ks`R%5#70
z58XuhWI<y+Cx>+$wnR%)_##z4XGa>6T6PO+8->CH^I0&$;4_@LSx|2{og$Z=k)dup
z-VU9|o`F-H5L9qYB_+J3!0f`$@I(V2wM*8Z`!Pe&rHM!3$Eq-R>6t4O$-cljt>}#w
zdo4g(P4Xzeqk=;(+lj_Ejz<;kboesv07{OGMbj*KoZ3(gj-mf}^g=2TrmLKxT2$YW
zNsU&hC^nd!yN*SEII|OuU4I+VhgHER8zzufwoQW0^EOk`HpSG$lku?N;t6t+<0;-D
zZ96{Nm5Mg*xeG@eO6A0kmO|2DV_?;YG~|VKlCdXZpw;{J)O0a@N>8E)u6*JOiA)jL
z>8Z@OtC8d%*ee1lLocdcbp!cv<3<Y3J%Y+qB?Wog4eJ6=p~y>KXx!ySD#%oaykoHp
z+GI(<m2q)o?uRSzfaM`Ljktg;uU&_V32)K&{C3LbfiHV*(`fj{sSG}vQACQ#Z9o%_
zjDlU&HE2B62tOS^3S+`3o~rx}NSfr4Lr({h(Np-asdf(aLe>%v5j~C;Pdm<A@N5;b
z6J3Yw7@DvsY8xdwLmQ=zFoWP>HoD_-k*Zr11H~1JDb2DfwDZMb6!9{aTx=!-L029;
zYsseW%nL>b^!C&!sGLQeX&b0#*jCgY`-B(zau?hrk_*RXu7P{qDERDd2Q@U@5S>hX
zhW?tPg_bs-C*I$<j@~cJq2k;RqidxmaOuJk+&M)OT*Vur+?^q>DJt?4V%Z#nZ$FRc
zH(RT7B}e+9$4)X_&Dtg;HT??mI~hz`1zm<ObI+r-#ajHleX1~HuN#bP=22pA<>0#E
zNhrG90;RW{MtdWVlQ9SHz`ai2(91X_R8y&mw&rBP{I7{TX5lO7_W3wsRvUA&svP+p
zb;fAto1IXMvWEWQh0xw0pQ<t`L}IN^IYi(mYWb05*zn~LG!0^N+9Kw_&;C#0q${%U
zHQo;IoLhrDr*=^^hr3YaYclMjS5VfAZ^8KDB53I^%}+C&k8H0n$d!RMP;2HKa^j2`
zu-57Yr4{&w%DJ0CZ4G8qld`u$nc+Lge7g~(X3jBksp3(p;NEWX?(r2+#b7h_`8bDM
zlsOH3?TqJG=BvO=^A)6uj0Z9WmtotS26DBL40pl4BZyw2!)>Z~3|G>ZptP`~NW}9L
zrL$O;A3k>?JkG-@abg{`m~s?Ocu<D|$8Uzsc8SofS&QpJ?x#K~52A)_La;a`0)4re
zg>2fcQAW9o(K7ueG*h0zE&t?0rth6Xj=oudTs($x!)x~;Coz4rZCnck)jMEHQxmBs
zH35lc_;R%xTG1%g4V<YOBe`y$1pEb#Xr$jgbpCZ7;wmMP(*{lC?jDv%E@pnGmM>fY
ztBcjSC5Nlw+2@&*!#r~sIYbW;I?XWqT0CX5!xq}rcT&zB>4;)#bGu|R;I&dE{tA<k
zsNGTvKDL#BuQiq+|G~RRFr0&Ek7Q8!{!YYw{}nEZlI3m{zX<iKc<_PhH14c1vAmBH
z?70o+cSEI1ThWcy)o^&_OxWNO2_yE;LtXybaJNYjuVn0PsN87B9enZ{x_0|D^4u=Q
zZQ81bc8uBuJDSfSo<SDO)0hXJWER4+0mop`wrsf2YZleGPl+3MS{BCfGpOAU<H@;;
zDUMG1bZG4yKxW$MaW73?1XYSGP+T$}O6y9Xo1eCln-#{Qxer~b7PYm|Uhr)@O-RD4
z7OkjOy_k%xHR2x%$>3&Gt8+ybWx{jr^4!5YW^*sMzoeqCEaz@Xwn48y2a|g@rIXT4
zv(cW{>eN-*EAnfQCUg&y<Gu);O|BnT4!xPBsNG^MHCn0>Qoi>{>Cpjjm-Tfdk~D>k
zsW(HHq=s_C^sKnkmN-*{T?TUbZp0U(L2j~?2KTskD4elVhfD0a3~kTn^QvjtFmua!
zI7+`3MYT<X-q&=f(O%9lX4Ouz_@OlSrpjKVHRmk4X%@qM?Ul~4o^Fif!dJn(72~)K
zR48iw^b9c%dDpDtSaL4k+6%kR#i6s0-H}JgX@r%XgC^N!=w8Em^8R5P^rkwFm(09?
zq~kQHjhdz$cb`MBn12pgH)(Q*v=_qF&0^HVmj+bbnFZ*4`xE5i-%LIu6k*JVjGE`C
zbdmhvD45(4L)v|B1CN(1LQgBKInu#<$v0c2Ap_@;-TOYbbK5B6JqGZLzYI)kbAlru
zN5LHjO}K{+tMa|G#rRVrhjOzPB%rob6YlG2HLyrxDtxHLK<V2T!_Qk!qbMH@RF<#=
zW>>Z&(<$QI?e>$oSs^oEi}Ep!yR#jCLWeVHFisMF(N^atDQWSajU0py`oD&rS<|=&
z{l7znb|s3rVlz6<5#>4@mFB-wOoh{~-zArKDxym>)%bz8kMQ)wSqQta4F$|`<I07~
zaIKl*yczWw=xJLC%-)y^cR49?_r6t!DOD;|_cPfl`oNO?Q)<o{s&J3%ll--Xirkea
zjQJ6Afe@Y2<OfTo!|N?u(YC`+QOeg;E;dAp`^i=x-5q%zbw*xAqox>e^+d9u=M%)6
zTa8F*@k?mF;JZBOs2|^U*kEqP+>@}d%Yna%RRR}bEl}nIkNYkmkiU3d6y$8|j-clk
z!iXihEk8d$uH(M3)*>QS<9gddmK|~S#ag!g@KJ<Oo<CvWu85Ctb>h5Mxr-|d9mKvS
zR);Uu*+rPGeM?kp;l!v$k~r!-g}r#F6=&9DW%lMWoR~h<nowwJCk&hrVW6kLc2~~_
zxx^S^O}q^LoMp*&dZWgkd{mG9_>B}hlcPcm`!<xF9x<J5YgNZK-#m*A*EJG>ixdfi
zjq*^V{sh}pmCJtFeuijwI>O#f#IXOmSV!2N_GPPgq_8K4Gsw+K4Qw;68hfQ$6mjOr
zFm_SqPGVC110q1r4KE6+BkqkgB&K?JuqPINWS`%$hy798gdJSv#C|$QhGVGJKy<y6
z<t&_;z+Nk3!!EoqlhCa8BC9$**<A&a?DLl<5(Tbw&dTZS?5<&ViDhGw*w0^juur;F
za>n?_v6Yk062~sJ65nAnVRu1+eOt<a-JEn9PkK~9(6{Ww)zhRon+%iL)T09SjT=ow
zlzk{We_|24ea&LfcK8WUX@g+yilIO@ISJf+@Bt&j^sw!mDXim(4y<mCG}{dD?siq=
zV4&b?2iQY*W8Hn_l(8&Y2@98%%)u(|GC;+sdT`pCMN|)aOi&@?alL27_{4XW;Ckw1
zFl6p{@H`=1i1&KgETQ%bVtDw>vIIOyBS(n;W}Xjba{MEqav~aJez}Ja_C1VG_T5ZK
zBxV!KKR>|h1}PDN({}^z*Fj)qJjPn0;b+nv$C^`Xg?O9~7jeB0Is{3V?TL4I3rlT7
z1kt5>3pBpDg<qXY$0zSJ!M_*Ena#f9jLrU{jq?}e0}okO;PqfTHgE4~(2bXd1q<)L
zv_k^u<IT+EdwYb>`%EkjvpJ+kj0|hRJ{{=5KH2AhphYqG!Hprf#m6$R*P#<seR07m
z>o&0X9h0rPvATc7w@rk2t|i5w)ldR2xP3^7x5RV{Gayw8ADz1$b6A=S3cfb8W^;})
zyYJaDr_t)md0nQ&;GwL?Z#Au^TgEf<WA%mkE0P{6yuTtxk-6V{sl~)eD#CksOVg|_
z6cl5sk$%k0S>Kp4qJ?0O=S9%^<{c~PMkrQ9&jZ#o=-`#cDlDb6g!N(kBlB*q342S0
z&z(=4#DeElVV(-Nh4`19kFZ8;I0BZ`q+)whRl)vkMnKYOE?y~f3!jzc3dC@2{3urm
z<b_0mm->7xbNU+LeV4Z63ZFN1<{*5{*EsNE?hPTni0MbxnAVAed|@%R8axBLN4Dd>
zOk=jv)}8F*Yi1D-mais`>=DC{4p+ufy||X$v3370G%2&YTgh(`M1I>2;^Ct#A^wMr
z+E|P~&K@yD9iLFKhv3)M;gzn{#Qi;&2y+)vVt<xC@!B{AoN6n_{JkTDIqJqqnw27)
zFV9zg#ob0hqD@gkh-W&J&yvcCB-Sl%0-2H`gtMC{(Kw$*yfCiD6#S||d1DRk<hLJB
z)IJWJHrs-b%4@>sI{Z38h<Ej66DSV50i;#$3a=L|)dEo|4aC&Q!8kWf443T8!!;zg
z;~A?(S(44NperE;Up(0hpSUvvsGn2;_TOp3IQ*SP3h}Z^oj?WaG#0wrM~L^PLkx_l
z-bCnHYzKC`{{qQ3OYsMiBZ#WVe9S0K8E|lUVpF3EKK0Ua{C;vBnDl0k5HCIDtgw}}
zjKbMa+DbyBQiwOo%^28ai?eTzbOWV@@4-IRPSCly6`yl#G4|O(AFCS`g<Cw92E!vs
z{OyDqARbQ$aZ2773U$uCzDC%G(Xm3ju}VWRb$t^;X|g`}W)cUmN4nUdxOrgsHxXPn
zbQmUbdn=~6A)Vzaa@X{U0hivbMfXo;++iW!M@|){q;>;yZCxjv=LzG^SYE~JFwdJK
zSzn4*Vy0$SL7J%s9+FIA6$w``12buSOUnk3#v2AW-=|<kWnRKKrl@8M<1!QZfF&Hx
z#UgCK2=UJior+1mlqWPszr|LYN`inzud#21Uicm7v%>qf>?#7Op{C%>IVt>(Tqcma
zKnUOAvgZ$l_kR*|zylmz%`djK3H?`Yna(1H9>61+Oi)gjz>G`2frE58yym7d#(4Dv
zli6GY46fvZJn<^bw$c<Un3W^Mk#C3-#`iJdEf%gQ1HR`!6<$B}UJ5hRsKC9u1_Of#
z86a_I3yyWsi5oYVdSBK^fokRnTpCn^zuKj6Z=WRKtan9-v!c{i*hY3L3fr2XRv{j<
z?hz(N>?h1}&ta>MKLHJ&4M2ix4S3a&j_C#_Vp0|hK*CUMj2>IXQV#31nou-D_&vpE
z7zv**WaVZoG))4G%Fz?z4N|+xlsp%N6)7;V{j1hsb8AN8T(dl)q1uKG8V=)0-(-lh
zY&vd~^ch&F&jZh?(Lx<@14=lbRU?&%ptE1_r;3k+aq^$#Vre2aY}wJLLB<OkV#Ts@
zmO|x8mQ1RSwo!&O^Ujb;R?&({tRW>2nN2lz%#e?9LcCE>S{RS(kyK{c(khF#qb9<4
zTW#YArgBFewmv<9If1!{X_2demq^<Y8A`Eu(#jH6ll(KBrTGoietV3|8HeG~^-aR(
z92V3rZ1-~vg>g>bTP}>RORNr4IC6~$c_e}-w!Z)YC>P`J^=Da5$kq3<+r~T;X2yEB
zSPjb!lw#g~rN`2!e;|ynaOMSJd=2l<GA-O9EnO`h3FDJ=_hS}b_X8?6qgkb%Ygi)<
z>VW2&u>{NP1HmzK$J>q-;MC_m!0K*1*gsPoL`rdm_kCT|vtGu2KZtkC+l=SdCJ5sz
zQ(<DO4z&^^7e@g4u-EwD)MmgLqd{bv#Sz0+h+=2Bsd$3CIR5VIW}Lay5D%JTD2(gg
z#ofaD=a-8R_g;R$))6O!{f+}Wv5)QNh-cd`gLmczxZC%MMAZo^g0G%}wa`kzSL05+
zV!sq|<02h*i`oT<Sv}u_$=_;)dE53?9G~><449RpB*b$&84Bi~QD-mhn1nyKVBxj-
z21K0dFrn|GW0Alrnnpz3&c~-NAB3wMC<lgV%EGa;?l!{KjlK)`tJ_)JiySA^he?YC
z%Id?}Uo3Wl$J2)6UQ-S5Q?_YfV|$HRgpwonxW)r~EjR_RL&LDcQN`G`nLC8{eaPuq
z&(GdW#_ZyhnO=B`Fc0!YrWolVLrBcIik*Hl7#!cn!m&vki0g+m*&1Rg_>+iIeAyab
z;4`t(Dx0(su9e;2E8_?qVSE{bPvCOquW_YQi9()R!@XI{?l1{fpaq!kd;?c;x<MRK
z+|8zWo@5_QuP3mIm23^|JmSjA1j4H%5hpElg*f9^4dHa-5=vrhd!GkH=fT^;>&r3%
z@MmLFIg4C|6K+>k*pHOshyeLqEJe4D@bLA<Uw+g8<L_94%SYA0mP|L|#><01Go8e8
zKg;7j8oAhXjy2JK_!Ec;P6KMArwY~~KBn(J2?T@}<1@$9V?~1Xhx0%Z8+`5y9+a{~
z@GsJG+#(_sth*8dhUY%SI}7h)r;a^f*^Q!DVJtevRyYRE4Uz(v9|+!YC`!cMU|=R5
zX;|PF1N=~l6<GM*6m-(gF~=FK$4-~!V0BZi!Ja!=%!vve7+p^u|J2ydtTZyQs%DEa
zJ;$^#{VV114<V~q?_Ez|276n;&Z;2RqKYnNlzWo!J$mLj6aUnp!HSE*ar>E~V8lo{
z{M(QjSaYHpHgfz7-1m(gw!6}kHGbN1Ouf^Ib@odE#$R*^^D!QS&0U0n&Du$<keBaS
z?ps5^$Lr--pnEydxaBsM=zN~=kH3j`rnlfpmvHca5rQkUj%DxGS_LMw1z<KxS-4gT
z1H4giVwcy8;3|wdaBICBTi^N+7=2qBtgG<E#~9{Xy|15!Te3=6rwVrBI)2N5l5qhz
zMl-<<y*iE+w!Of0I!0ql0*ABagr@+V@Pn*&4|!tJ&3LSF-2_Zd+ZES>^RT=L?X2WE
zT439EX)JeaE&h6}C9tqHW~Z(mg<rWFfIW3j!Dnqe3d#zfv1~TV;=8Qtu_O7qU}P2z
zR7{h=mPRE2`O%;8J0%#qcIOoA16&4L9Ic6i$MOJB$M6P^RP3rw46%Ad2{>K)naOTB
z04}%df=gZ*So2MNu;ZRIc6Eg)2;*2|_F@Mx_g%NK_Bq4w%&dciUDqL2?a3spRA(+;
zmYsrGf4aqdAz5TK{Ph;>MU|0IqfDPj=2OpF;djc)K$WR>Jlg8%@dM2G?PGh^P9+*M
zq{N>&v`hvx+b^)XGTFz{Fe#6@Pi?tr_niN~{GG$M6Ib$$u|;(i_?o0jyexAAajnS~
zoJ&r`*Ex)1^AFF%?6&w}Z4R5jqX(VX!$w)Q->YljukCH1Oj({?uqzj2_={nqW{kk;
zWg5bF`u{S%(X&K}1uy(Dj(h@f!Wn^a=V#;eQ`O+Z4g)YMH49HSUJI7c%`jO-dC;{p
z0$ZO;f{U4jVBg6>xWO?FF|kPj+jIB|wka$P_p_P;KGCk@ZE7vdmlbYUOGP$FoUKM&
z`O690s_g_WIgTSnZZK!**r%|jOAWyqROYf&FJ^&z_4;79Z7guz_W+dKSH#AJ-DFCk
zSJ>&5ieP@4J>gjL43m(SW!uK%_~UnN*fO6X_{w2k;AH3?{ON(0U{^#cHa99B+)%xO
z(NeeI!S)|PWmB{u_IBc0dnEQYb3EAnO|Z^~a{kZrx6I=-PBf`u6IeRLX7fwnvA!~M
zs`D^xjtmd;`}`I^sJj`P>^Yugu<{9rYPiKRePBeiGH+qK{R}Yg`ay(O=~}F1vN@(}
z?+tXsi>y+8rw|T)hq3h5mqh-DSKzM|6Ii<(t^?JNk1?kqBXD`Aw_tBzGNvar6AVvH
z#H6=;2IroO;M@P2g!>h62*s8<mIEgN%zWL6gRJ?$_4`JA`uSO`Z{;!gJ~u<)P-=<K
zx)O@bpH<H)T9=Ivez6eCejCW@P*nsm+4-z9&-{S0Ll^e41hJg1Gy|GuCu;|7G}byZ
z3A9X+0}H#R;0kXpFqgZJ0LC5(peD}+_n7jDwfWwBpg&82ShL|CbMCEh7W1V7mc^=P
zRXTU#kBPNd{pW31<fC@n{beXd`%-JQskQ*K8Ges--#VCmkh=j?*A%l4mpK!ir=}CL
z9m8-=eH>o1F`g}3xDnTMF$M;w-{Y^IYy#7UJF_P|e~4e5RfvC)T+6Q7+YBUi82Ebe
z?L?>JMWMcTPV6EyB9p<I=4(XJHGObT(ui2=P*3nIw&Q*=F~WU7_dCR0<%wS(!^5>L
zcjD0uN0!dneR%ASRzgg1Bym{2XaC*p*KzMOe&LfY;Toz*1fIXZ(o!Ev+&<*Mt`hvC
z{&6tu!<(_t6pxbJhT9K`$IH(KVtx-}aO=E$Vui?K+^jvhZ|)H$Ho$`1im?u)gUL7Z
zEXRz}2C@^*fX;>?;7PKMa39dkn`to^*EReMcK8S2_YU7-X?@keo%gllrr#{^D_!gR
z{HDcR29H#m!20i*;Jcv4e!4>N%~-Yt_f@C?4@Zji@tPi(j|bd;gT0kof~y>B!lYUI
zz_ekyxP*QcYlb%L^Rw}QxYJn`;+|?e?w|6R^=QWl{G|L~wur40anhrwzU+=~k?TZU
z;WG;_-I9*CT%M1O+dT=_m*WtJSFFOXGJ5t$-G0iRVff%(Lx@4&%5n3Vh&5^G1ANMI
zRrZ>8A41ykMIZ0=a4kHT?+hA`JK@0}Mqqw#>Oj-6AOXiJAa(Li-}7Fi-^FGGMYG0l
z`GDDv(#D*-^fAYlYz&-2R$nGJ3)g^d-U;4u#QD~B;I-op!hC%q*aY6<>Br)U6W2L7
z>#B2~ZY<6^R-uv))^(?aX@30Tn9sqPEYUm#U=ZnmJvr~*hlg4Z14)|@;AAO^OV2OG
zzS^dO!;Ln$oktiL6TYD@K3k3$Zt?jUR((<*pHvux)p@0X@)JYx#|v7pRn6snera>;
zF!`00m`VHs;k7vFbZlkp6QFf?D3Cr$@AF%b{T!qe`+=BLdz^FM$IA7oGJY3o5>pdq
z;$utI`ts*Aah=uSC^6=fzpnJGb9b|?T)NIOIU1VG52cL0vmfnE=ZIzNqzGLPS@y|L
zcQ7wBhmhqiW7n2s5O_G%mzVAw{`f0bDiNM}bYJtB8^RF}UBj6tbF44Ui7NZ>;Uzop
z;_{JwF&P?PAv9Zyh>Dae7QI*s2aYg14IeXe?yT(dQ<EOa{(4)SO}ro1$NzQlX11@|
zYW9P;ZNj~0_x<HATI|TXhU|+r<$bYueu?79vJ-^+)n1O?Hmj&-|K8oF?z^znjyST#
zo;9)Sh70!8E2jPSo^Id2B+s*BH{4*0Th(&nE$Z0;Zyth(d+mZV%6cNnr2wzJ;lR%E
z@!&MOqzcdF<-)CroVg=OI*4YQwe4mbXLH#V%CCu!b4@r|1+Uq6tRGj^9Hs9SqpQ(G
z8OlLEfBA-aFAIq9_g>~3u)see+{BDNSTG_b91(QXXoG(1W-f3W)azz0a1;Bjn}xtl
ztk=y#;3ocCH%oz=c(0qKz)j+}ZdL*}iC#A=ft%!S-IxM5$zC_6z)kA6ZY+VDRIeLL
z;70qc8zyk0^}1nnS(-FMI%IJ~z@mWVzF~BA8l9mM=DT1?kWW~^a-WERkYMkiknnKd
zaJnjOFheOK%qJk&XKs+M_fmmFw-GLYkYPyw*p+2SFZ2!b3HSC{96*<&$ua2R0SkhC
zB9?>+y#48Pnmj{N;1KK^M5oad7~*pSe8L6Hiv2S)`eRo5nOXTq=58jHADD*pF{$>?
zq}Csk`p--n|IMWN1Cv%ClXm}1I{h)}{>(J=znO;pz%;y%X+-}_Bl~00`<ZFfe>3U-
zz+}+JWY|BGQGZOMe`YfNZzhu;n8x%mnfA|Q)*qAk&rBBo&1CrllT{xRvwtR5e@xiV
zOyIwn@E@3nJ|=emOq~9h$e)?uk4&FM1uqeoAs!GRczva5)(naHzVm$kqASwIGGv2%
zeZqnRf){v)`9%2AWoR}G+M<v}zQGYo7SW|>;}~LL{vjIDg7;A@Y*Dycl)zNzF;@yq
zl^!!%U=HaqS832`nmuNWsKD0hu~&E7!+PvB-S+SvJGR@_@3G^$ZG#>=zS}nGvDbFn
zV|wg_ZrifQPVBa=dhDcb8}G4`yKSP!P7$Sx(3jFB1o4ZS2^s~4sJWm~VTf7?njs8P
zOF^T_5VaCCS`1O9pc%#xWeJ+$3{gzb=rcrtpfO;G;&e$tM1#!D1nn3>Yc6Ol1+9gk
zwGy<Jf)*FFR)Urgw9Nl+yaz<-nzZo@)nLJw`FCIA!8BWj)FR)Auz-2pAJu+8=eo2B
z49%Ve^Y`C-McPEc1QM>Oy>NfJ1kh=A3`yaw;R1!+8E$krx3Sah$J#j9db^EvaJ6N~
z%=Zlr_ciTV1vFSaG(Ruge-E0D8SCOQ-QLF4#n##MXB4^%WBM=1^d98yHATbfx47Ly
z1EGr0<ph)B$2s-ieW3>S18FenM-BR2*mP-=|49RfUp4r%#gI;$(x-tZ!~K^$$qDo1
zZ9b3&y@LZ~%Z=gk3wDpX0_=WO=ouOaw?_rRyWIU|3*KdOp(;Q2g(^%PNCn3qRp@uo
z*QHJSCl#E2RpHMz3Uu1^KT*MAAQgHC2g=p}Dp>TdLeJ1ZxIHQezM<VJ2)>~`D*V_N
zs^C133NAmY(C>ywm*)CUD!Bcs!k=xT=(HJsqJrf>D)bHxl&t|&u<T!jo}qzodsGmt
zUEL}O)~+5Ee(VcXa34qoj~`X&ceAKV^ZX|jX8x+epKUPdv{`?mg4IAO^bQV`tpQZ9
z>R*MPp@DFFR1oYLx>XSD8G2Osu`g7?YakV7|ENO08);pd_dlsH=T{Z}Z1YX0`TU6r
z%z;$s9ULfI1E|34Uxl8bfpB|NFzHcYOs@(*_Ju0U9Y}?FKdR7gk)cbQ|4%CT{;I;C
z6()3=-=C<!8c2oS!GW?hfC{YsRp=QS2)9QC%N`Z1dR6$bFH~W{Kq~nEs6xL5m@X~g
zpHv9^RfRt*+UT@}f1(05kP5wn17&Lf6|nwQ=ouOaw?_rMM+Kr+g&+Gu6@ms*VbPB&
z^jn<j(t`g<g^*uW__KnRPFvik!eoa1ue-_VQ=Auvg)BDhE^##s`wpvw{ejXmg)#XT
zFxg%(VU?_5^dB_gg@NF_7&Cu?Q|X2KsnXW4_z&z)0|UW3Gn{^bSLlWBt=ctA{)60m
zZ6Hv4#>8Jh2ls+@7ykzT!4q@`g6f%Up*@o=tb4MF2>!b3XVIQ(gSxLt2+l`^<+pfG
z`7O~?eoOY0-%>s0H?6z;?!No~^3O_e`Vr2MhzN=B2@;qQ42gLmOM)W=$CFEbK0aCc
z<MGLOf$OsW+!fh-mmKXc2Ho2)*n8nJpRfhphn&lQMp^MAiX&snFKO<HM8m4@DAe@t
zGTPM4R2bwy$^I>%@Ogd@C>$CHu)Bl}@6jouTPOMd&dYBZg>n9l(ft4V5zI~h@ez6^
z;If{Fi0plcpJa&@WyFa7qJ&!Sg#Yo-!BlYKrQ!111A?AY3DYr-(_HMQ*n8Se_jY!0
zbTR$mO<6SH0YWq*>X#UX{2YVu45B-N>A#Jj=ZNBe<&PI-tobzy8b3wQSNQjvhyN7+
z|7#pEqKxQY67jPbf1eQugZN!4dd~^|*AV_F6@3MEe^a8*;eVbIf0By7tMUG3MQ^eG
zuco4}%Iz-|eGdO>D*i5s`%6V{#r&_PqOa8KFBN?b|7t4!u1ot%MQ_>quco4}-s>+F
zeGdO>D*i4G`%6V{?f9>zqOa)cFBN?b|7t4!uF(2RMQ;K3uco4}^64)XeGdO>D*i5$
O`b$M`74<)+;{O4q!uSpV

literal 0
HcmV?d00001

diff --git a/assignment_1/deliverable/nonlinear_model/variables/variables.index b/assignment_1/deliverable/nonlinear_model/variables/variables.index
new file mode 100644
index 0000000000000000000000000000000000000000..20b344777d2bd8cca8477249cfaa90042f910cab
GIT binary patch
literal 2850
zcmb`JT}Tu`9KdJqw6-NSI`7UtuhA<Q^J@LtyNIYxyY%Ynj`K7PVq0y~CGXDeR{MhT
zp&*DbLNY?iEUE_!k?0F4A;_yF3Vg6{vmUYtq@L7*W<0VNIoGy**nwf@H~;<3e`o#x
z%%G_SI+_BD<_HBvu7b@zuX}62neTB4-uz9jf`CxyclwIxnFau_m7~G+eEqE}^FbcF
zM~aA1L9CUzprl0P$|#q`Rfg+A3b)c32>9LkB>`74SL*b;$6=*TPl=1BFy)O6CGkTk
zR-X~FN>~wVrkc=CCL~p^Ib=v@v(>HfX}W6+{8<C!8EV7>QbgD%E7ih2Nv^6^qARkv
z3R#RM4m8Q2qb+clLi_ohBhl>(9R{y2;NIfi?(z%89&dmP*D7*QJ}5=FNKh54e<fzs
zW_Grj(u`PlNSZ<wjU&*JD9|v)pL^Rlw%C#S?=ei!;Aa~FvS{7qlr%%ON)<*JGa(;8
zFm~0WS%ndH<YU&5z3rPREmf@w3szx-pLJ-!z_&jA)TG%)9Qd3gz|)T{jo3zxY8(CO
z_zR9YHhlOGHoCo-)%f925wNFDlC3$MT1CcjMQ6>Zdp7??wC>QXC=P5WAV9jStsN`M
zRjG)9u?&Rx(a>nMW<?C_D8;N-%XW*?NEi}41Fu<8>kR(K_1z=?#RdaU`3P`?HRoa*
zc`9ur!#kPC$Wxb!PieN13|q@E%W{5@nT8Dp+en7P87M~a^;Slw*+v}LUrK<_?yjP+
z4PK=UJv?qhv-y+yq1&2m=;3e~X7zbCTrj1X)&42T*oGeVW}-Fx!@h>j#SXK!lQqt}
zS%!J1g=2o~!miSV4z}A6%|AKQ^i{J99c(JctdGslY`|`w<S4PLK1%dh5XRZiJbuyr
zmYtdf#d$x=Ft5ma_z4!|P$`IlUF0@Da$~LbWTD{Ya#DS~%xIoQvXD!df;Y)F9j@5h
zquE8A*Fv_6u8;MB<?P>DVwY4Y9!Ol702m`et{j@Xnpwii3a&sB)M&GCjn+*<m&@-2
z7{t~{Vk9Jos)UHBNQowr;;xr0BtI?5VXSO09F#&zU2v*7&nJSju#xKURKxqo@Lykh
z-UIwR&;!8G0N@6IT^sKmCoM%@-QO>E07_pAdQB63&7FhP3lsg$G>3F0ezuJ@oLE{{
G`1&_rWeBeT

literal 0
HcmV?d00001

diff --git a/assignment_1/deliverable/nonlinear_model_normalizers.pickle b/assignment_1/deliverable/nonlinear_model_normalizers.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..4e9916fc9deaf305a2cfe419f0bfc424cc25c1b5
GIT binary patch
literal 278
zcmZo*nYx<+0&1sd^swZnCgx4)5zfj_%E?U9%PY+-sEjYjOwP_pozf%d2jV&w6(v@N
z7bO-Hq!vw?JVmo{3P>YIacNR=PGWKKlpa>Ft|>k2c`1n?wLnEZti>6L1*ucKnOcGD
z{Gt?~)*eP@AUma`5-6U*0b>ExGNoBe>Fj8q5;R4_o4JS4W{RJmpVxmN02AH}B~y}m
zxDs=6^2_6Ma}x`ubd)ABiOTjnYqB^Td3C|}_Bm#Usj5&v^)MHgq)f?Rfw(e*J%cNQ
m8{~)#-VDAB{tS@}aiH#$k2N`GFMPH)UK}8BBl5F-sU83~BW;EN

literal 0
HcmV?d00001

diff --git a/assignment_1/deliverable/run_model.py b/assignment_1/deliverable/run_model.py
old mode 100644
new mode 100755
index 9b2d6ef..d4808f3
--- a/assignment_1/deliverable/run_model.py
+++ b/assignment_1/deliverable/run_model.py
@@ -1,6 +1,10 @@
+#!/usr/bin/env python3
+
 import joblib
 import numpy as np
-
+from keras import models
+import sys
+from keras.models import Model
 
 def load_data(filename):
     """
@@ -57,13 +61,51 @@ if __name__ == '__main__':
     # EDITABLE SECTION OF THE SCRIPT: if you need to edit the script, do it here
     ############################################################################
 
-    # Load the trained model
-    baseline_model_path = './baseline_model.pickle'
-    baseline_model = load_model(baseline_model_path)
+    ms = {"baseline", "linear", "nonlinear"}
 
-    # Predict on the given samples
-    y_pred = baseline_model.predict(x)
+    if len(sys.argv) != 2:
+        print("%s {model_name}" % sys.argv[0])
+        sys.exit(1)
 
+    if sys.argv[1] not in ms:
+        print("model name must be one of " + str(ms))
+        sys.exit(2)
+
+    y_pred = None
+    if sys.argv[1] == "baseline":
+        # Load the trained model
+        baseline_model_path = './baseline_model.pickle'
+        baseline_model = load_model(baseline_model_path)
+
+        # Predict on the given samples
+        y_pred = baseline_model.predict(x)
+    else:
+        X = np.zeros([x.shape[0], 5])
+        X[:, 0] = 1
+        X[:, 1:3] = x
+        X[:, 3] = x[:, 0] * x[:, 1]
+        X[:, 4] = np.sin(x[:, 0])
+
+        if sys.argv[1] == "linear":
+            # Load the linear model
+            linear_model_path = './linear_regression.pickle'
+            linear_model = load_model(linear_model_path)
+
+            # Predict on the given samples
+            y_pred = linear_model.predict(X)
+        else:
+            # Load saved Keras model
+            nonlinear_model = models.load_model('./nonlinear_model')
+            # Load saved normalizing factors for Xs
+            normalizers = load_model('./nonlinear_model_normalizers.pickle')
+            # Normalize Xs with normalizing factors
+            X = X[:, 1:3]
+            X -= normalizers["mean"]
+            X /= normalizers["std"]
+
+            # Run model on normalized data
+            y_pred = nonlinear_model.predict(X)
+            y_pred = y_pred[:, 0]
 
     ############################################################################
     # STOP EDITABLE SECTION: do not modify anything below this point.
diff --git a/assignment_1/src/t1.py b/assignment_1/src/build_models.py
similarity index 73%
rename from assignment_1/src/t1.py
rename to assignment_1/src/build_models.py
index f980fb3..f2971de 100644
--- a/assignment_1/src/t1.py
+++ b/assignment_1/src/build_models.py
@@ -2,14 +2,18 @@ import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn.model_selection import train_test_split
 from tensorflow.keras import Input, Model
-from tensorflow.keras.models import Sequential
+from tensorflow.keras.models import Sequential, save_model
 from tensorflow.keras.layers import Dense
 from tensorflow.keras import losses
 from tensorflow import keras
 import tensorflow as tf
 from sklearn.metrics import mean_squared_error
+from utils import save_sklearn_model, save_keras_model
+import os
 
-data = np.load("../data/data.npz")
+d = os.path.dirname(__file__)
+
+data = np.load(d + "/../data/data.npz")
 xs = data["x"] # 2000x2
 y = data["y"] # 2000x1
 points = 2000
@@ -43,6 +47,8 @@ print("# Linear regression:")
 # Print the resulting parameters
 print("f(x) = %g + %g * x_1 + %g * x_2 + %g * x_1 * x_2 + %g * sin(x_1)" % tuple(reg.coef_))
 
+save_sklearn_model(reg, d + "/../deliverable/linear_regression.pickle")
+
 # Test using MSQ on test set
 score = reg.score(X_test, y_test)
 print("MSQ error on test set is: %g" % (score))
@@ -53,48 +59,45 @@ print("\n# Feed-forward NN:")
 
 A = X_val
 
-X_train = X_train[:, 1:]
-X_val = X_val[:, 1:]
+X_train = X_train[:, 1:3]
+X_val = X_val[:, 1:3]
 
 # X_train = X_train[:, 1:3]
 # X_val = X_val[:, 1:3]
 
 mean = np.mean(X_train, axis=0)
 std = np.std(X_train, axis=0)
-#y_mean = np.mean(y_train, axis=0)
-#y_std = np.std(y_train, axis=0)
-
-y_mean = 0
-y_std = 1
 
 X_train -= mean
 X_train /= std
-y_train -= y_mean
-y_train /= y_std
 
 X_val -= mean
 X_val /= std
-y_val -= y_mean
-y_val /= y_std
 
 network = Sequential()
-network.add(Dense(35, activation='relu'))
+network.add(Dense(30, activation='relu'))
+network.add(Dense(20, activation='relu'))
+network.add(Dense(20, activation='relu'))
 network.add(Dense(10, activation='relu'))
+network.add(Dense(5, activation='relu'))
 network.add(Dense(3, activation='relu'))
+network.add(Dense(2, activation='relu'))
 network.add(Dense(1, activation='linear'))
 network.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])
 
-epochs = 10000
-callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=1000)
-network.fit(X_train, y_train, epochs=epochs, verbose=1, batch_size=100, validation_data=(X_val, y_val), callbacks=[callback])
+epochs = 100000
+callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=40)
+network.fit(X_train, y_train, epochs=epochs, verbose=1, batch_size=15,
+        validation_data=(X_val, y_val), callbacks=[callback])
+
+network.save(d + "/../deliverable/nonlinear_model")
+save_sklearn_model({"mean": mean, "std": std}, d + "/../deliverable/nonlinear_model_normalizers.pickle")
 
 msq = mean_squared_error(network.predict(X_val), y_val)
 print(msq)
 
-X_test = X_test[:, 1:]
+X_test = X_test[:, 1:3]
 X_test -= mean
 X_test /= std
-y_test -= y_mean
-y_test /= y_std
 msq = mean_squared_error(network.predict(X_test), y_test)
 print(msq)