Function_in_Python.ipynb

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      "authorship_tag": "ABX9TyPnEl6Z9sfqIur2QIlD01Wq",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Moksh45/Machine-Learning-Code/blob/main/Function_in_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Fs7hP9LqQhZn"
      },
      "outputs": [],
      "source": [
        "def hello(*args,**args2):\n",
        "  print(args)\n",
        "  print(args2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hello(\"Rohan\",\"Karan\",age=33,dob=1994)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmIY73SaRvRA",
        "outputId": "d50f1095-17f9-4eca-bfbf-723bf1833bc9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Rohan', 'Karan')\n",
            "{'age': 33, 'dob': 1994}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lst=['Rohan','Karan']\n",
        "dict_arg={\"age\":55,\"dob\":1987}"
      ],
      "metadata": {
        "id": "VMx4fgVOR8l9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hello(lst,dict_arg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biNXaLJgSKZp",
        "outputId": "412f10bf-1620-4380-adde-2e86f5d8938c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(['Rohan', 'Karan'], {'age': 55, 'dob': 1987})\n",
            "{}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hello(*lst,**dict_arg)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "47cTZV6FSTwn",
        "outputId": "db7a6a36-ea23-4d79-fd17-18df16a3cc11"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Rohan', 'Karan')\n",
            "{'age': 55, 'dob': 1987}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Map Function**"
      ],
      "metadata": {
        "id": "5YQA-q3nTkKk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def even_or_odd(num):\n",
        "  if num%2==0:\n",
        "    return \"The Number {} is Even\".format(num)\n",
        "  else:\n",
        "    return \"The Number {} is odd\".format(num)"
      ],
      "metadata": {
        "id": "T4noOl9RSbLb"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst=[1,2,3,4,5,7,8,9,10,23,22,53,64,66,43]"
      ],
      "metadata": {
        "id": "MFenGDk4UN3J"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#pass lst in function use map\n",
        "list(map(even_or_odd,lst))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mo2l5-tJUUzO",
        "outputId": "269c35f3-6cda-4774-cddb-d63ee9ee9f2a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['The Number 1 is Even',\n",
              " 'The Number 2 is Even',\n",
              " 'The Number 3 is Even',\n",
              " 'The Number 4 is Even',\n",
              " 'The Number 5 is Even',\n",
              " 'The Number 7 is Even',\n",
              " 'The Number 8 is Even',\n",
              " 'The Number 9 is Even',\n",
              " 'The Number 10 is Even',\n",
              " 'The Number 23 is Even',\n",
              " 'The Number 22 is Even',\n",
              " 'The Number 53 is Even',\n",
              " 'The Number 64 is Even',\n",
              " 'The Number 66 is Even',\n",
              " 'The Number 43 is Even']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Lambda Function**"
      ],
      "metadata": {
        "id": "1qcuFsj4VEVk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Normal Function\n",
        "def addition(a,b):\n",
        "  return a+b"
      ],
      "metadata": {
        "id": "To3LODYfVCmd"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "addition(4,5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCjBS8rBVtvA",
        "outputId": "c75d04e9-2051-4834-bae8-b8ccc36fa348"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#example of lambda Function \n",
        "#lambda Function is Fast \n",
        "# also know as anonymous Function\n",
        "addition=lambda a,b:a+b"
      ],
      "metadata": {
        "id": "DkFyUuMBV68t"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "addition(12,14)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMz2IGWRWA8J",
        "outputId": "ed344505-b9b2-43ef-e0b5-9ad6cd72b0c2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Filter Function in Python**"
      ],
      "metadata": {
        "id": "9AaBT5qaWyYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def even(num):\n",
        "  if num%2==0:\n",
        "    return True"
      ],
      "metadata": {
        "id": "RiSlH9PtW5Oc"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst=[1,2,3,4,5,6,7,8,9,0]"
      ],
      "metadata": {
        "id": "lSZkL7y7XL7G"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list(filter(even,lst))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tq9zW2YDXTq6",
        "outputId": "1f9fdc29-c126-42c1-9223-3f4e1b7ce388"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6, 8, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list(map(even,lst))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_H5sirZ0XdYL",
        "outputId": "c44d45d2-7bdc-4ebf-c469-961a5d4a39bd"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[None, True, None, True, None, True, None, True, None, True]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **List Comprehension**"
      ],
      "metadata": {
        "id": "Q_AKT8yIYjYI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lst1=[]\n",
        "def lst_square(lst):\n",
        "  for i in lst:\n",
        "    lst1.append(i*i)\n",
        "  return lst1"
      ],
      "metadata": {
        "id": "kP5sDNd_XrNN"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst_square([1,2,3,4,5,5,6,7,8,9])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvcN6CXmZeuT",
        "outputId": "0106b6f0-1f25-4ff4-94a5-cf6e33af58dc"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 4, 9, 16, 25, 25, 36, 49, 64, 81]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#same function using list comprehension\n",
        "lst=[1,2,3,4,5,6,7,8,9]\n",
        "list1=[i*i for i in lst]"
      ],
      "metadata": {
        "id": "nl4La_L_ZlEU"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(list1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHFLF4jcaRrG",
        "outputId": "b596568a-4930-4495-852a-5877dd928c07"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 4, 9, 16, 25, 36, 49, 64, 81]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "list1=[i*i for i in lst if i%2==0]\n",
        "print(list1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FYEQRk4HaXs_",
        "outputId": "1441a685-b32e-4ebf-d154-f25da84a0200"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4, 16, 36, 64]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **String Formatting**"
      ],
      "metadata": {
        "id": "PWikxHUXeReO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fun(name):\n",
        "  return \"hello {}.Welcome to the git community\".format(name)"
      ],
      "metadata": {
        "id": "jqUFkxdjeZyQ"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fun(\"Moksh\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "bEabtOEdh_5h",
        "outputId": "45ec358e-26d2-441d-c99e-a7de6f1913fc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hello Moksh.Welcome to the git community'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def Welcome_email(name,age):\n",
        "  return \"hello {name}.Your age is {age}\".format(age=age,name=name)"
      ],
      "metadata": {
        "id": "hkolEePziFM-"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Welcome_email(\"Shruti\",22)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "nFEgYzAPiZgL",
        "outputId": "9e750604-a462-4a71-e905-4717198c2d81"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'hello Shruti.Your age is 22'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Iterables vs Iterators**"
      ],
      "metadata": {
        "id": "Ir9BGL89jCgw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#list is Iterable\n",
        "lst=[1,2,3,4,5,6,7,8]"
      ],
      "metadata": {
        "id": "0-yuPo5vjJ0N"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in lst:\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5s0fvmnj0p-",
        "outputId": "e7946631-e22d-469a-a049-4671ad5cbedf"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#iter function use to convert list into Iterators\n",
        "lst1=iter(lst)"
      ],
      "metadata": {
        "id": "Ww_W2lToj5FL"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lst1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "si1wcjA5kC8J",
        "outputId": "0c922e33-7e49-4518-955a-fb372404eec2"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<list_iterator at 0x7f0d62dbe1c0>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "next(lst1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h941f5G-kExc",
        "outputId": "021ce19c-d768-47db-fbd2-eb804a29a398"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in lst1:\n",
        "  print (i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CmyRqPFknro",
        "outputId": "d0e57835-c08b-4c3d-fc84-7bfd4846a884"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **pyforest Lib but not Work in Colab**\n"
      ],
      "metadata": {
        "id": "5j_XBE9wnR6E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pyforest #if be use this lib we not have to import numpy matplotlib etc lib in project"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0b01KBsmAHo",
        "outputId": "b692ac6d-a9ed-4acd-f67d-9165c38ea6b3"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pyforest in /usr/local/lib/python3.8/dist-packages (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "l1=[1,2,3,4,5]\n",
        "l2=[10,20,30,40,50]\n"
      ],
      "metadata": {
        "id": "Cgqd-la9mLis"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "myhlhjJsmgCB"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.array([1,2,3,4])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sM8FGnb4mqJ7",
        "outputId": "0df81d87-3327-4f8d-a4db-eec06b979101"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(l1,l2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "msqjXlNFmZ6r",
        "outputId": "5dec3426-49f2-4605-859e-ed51bc3f3fa0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0d61e60040>]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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6CTx+zakMT0nUXrecNBW4SBR9uXE3Y6ekszp3PyNPbcPdw3vSpF7NoGNJJaECF4mCg4eL+N3cVTz78TpaNazNszemcnZ3DZ+SyFKBi0TYx9k7SJuWzqZdh7h2SBJjL+xOAw2fkihQgYtEyN5DhTw8ezmvfrGJDs3q8droIQzu2DToWFKJqcBFImDusq3c9WYmO/YX8KNvd+RX53aldkL1oGNJJacCFzkJ2/MKGP/WMt5Oz6F7qwY8fUMqKW01fEoqhgpc5AS4O28u2cJ9b2VxsKCY287ryq3DOpFQXcOnpOKowEXKacueQ9w5PYMFK7dzalLp8KkuLTV8SiqeClwkTCUlzsufb2TC7OWUONwzvCc3nJ6s4VMSGBW4SBjWbt9P2tQMPl+/izM6N+Phy/vQromGT0mwVOAi36CouISn/r6OSe+vonaNajxyRQpXDmiry+AlJqjARY4h6+t9jJm6lMwt+7igV0vuv7Q3LRpq+JTEDhW4yFHyC4v54wfZPPnhGhrXTeCJ7/fnot6ttNctMSecW6rVBhYCtULLT3H3e82sA/Aq0BRYDFzn7oejGVYk2hZv2MWYKems2X6Ay/u34e5LenKKhk9JjApnD7wAONvd95tZAvCRmb0D/BqY5O6vmtmTwM3An6OYVSRqDhQU8b/vruSFT9bTulEdnr9pIMO6tQg6lsg3CueWag7sDz1NCP1y4Gzge6HXXwDGowKXOPT31dsZNy2DzbsPcf1p7RlzYXfq19LRRYl9Yf0tDd3QeDHQGfgTsAbY4+5FoUU2A22O8bWjgdEASUlJJ5tXJGL2Hizk/rezmLJ4Mx2b1eP1H53GoA5Ngo4lErawCtzdi4F+ZtYYmA50D3cF7j4ZmAyQmprqJxJSJNLmZOZw94xl7DpwmJ8M68TPz+mi4VMSd8r1c6K77zGz+cBpQGMzqxHaC28LbIlGQJFIys3L594Zy3gncys9Exvy3I0D6d2mUdCxRE5IOGehNAcKQ+VdBzgPmAjMB66g9EyUG4AZ0QwqcjLcnalfbuH+WVkcKizm9gu6MfrMjho+JXEtnD3wROCF0HHwasDr7j7LzLKAV83sAeAr4Jko5hQ5YZt3H+SO6ZksXLWdAe1PYeKoFDq3qB90LJGTFs5ZKOnAqWW8vhYYFI1QIpFQUuK89OkGJs5ZAcB9I3px3ZD2VNPwKakkdK6UVErZuftJm5rOog27+VaXZjw0UsOnpPJRgUulUlhcwuSFa3ns/dXUqVmdR6/sy6j+bXQZvFRKKnCpNDK37GXMlHSycvZxcZ9WjB/RixYNNHxKKi8VuMS9/MJiHpu3mskL13JK3Zo8eW1/LuydGHQskahTgUtc+2L9LsZOSWftjgNcOaAtd13Sk0Z1E4KOJVIhVOASl/YXFPHInBW8+MkG2jSuw4s/GMSZXZsHHUukQqnAJe58uGo7d0zL4Ou9h7jx9GRuv6Ab9TR8Sqog/a2XuLH7wGHufzuLaV9uoVPzeky59TQGtNfwKam6VOAS89yddzK3cs+MTPYcLOSnZ3Xmp2d31vApqfJU4BLTcvflc/eMTN5dto3ebRrywg8G0au1hk+JgApcYpS788bizTwwK4v8ohLGXtidH36rAzU0fErkX1TgEnM27TrIuGkZfJS9g0HJTZgwqg8dm2v4lMjRVOASM4pLnBc/Wc8jc1ZSzeD+S3vx/cEaPiVyLCpwiQnZuXmMmZLOlxv3MKxbcx4c2Yc2jesEHUskpqnAJVCFxSX85cM1/N+8bOrWqs6kq/pyWT8NnxIJhwpcApOxeS+3T1nKiq15XJKSyH0jetGsfq2gY4nEjXBuqdYOeBFoCTgw2d0fM7PxwA+B7aFF73D32dEKKpVHfmExk95fxVML19Ksfi3+ct0ALujVKuhYInEnnD3wIuA2d//SzBoAi83svdB7k9z90ejFk8rms7U7SZuWwbodB7gqtR13XNKDRnU0fErkRIRzS7UcICf0OM/MlgNtoh1MKpe8/EImzlnBXz/dSLsmdXj5lsEM7dws6Fgica1cx8DNLJnS+2N+BgwFfmpm1wOLKN1L313G14wGRgMkJSWdZFyJR/NX5HLn9Axy9uVz8xkduO38rtStqY9fRE6WuXt4C5rVBz4EHnT3aWbWEthB6XHx+4FEd//BN/0ZqampvmjRopOMLPFi14HD3D8ri+lfbaFLi/pMvCKF/kmnBB1LJO6Y2WJ3Tz369bB2g8wsAZgKvOzu0wDcfdsR7z8FzIpQVolz7s7bGTncO2MZew8V8vNzuvA/Z3WiVg0NnxKJpHDOQjHgGWC5u//+iNcTQ8fHAUYCmdGJKPFk27587nozk/eytpHSthF/vWUwPRIbBh1LpFIKZw98KHAdkGFmS0Kv3QFcY2b9KD2Esh74UVQSSlxwd177YhMPzl7O4aIS7ri4Oz8YquFTItEUzlkoHwFlXRanc74FgI07D5I2LZ1/rNnJ4A5NmDgqheRm9YKOJVLp6VQAOWHFJc5zH6/j0bkrqVGtGg+O7M01A5M0fEqkgqjA5YSs2lY6fGrJpj2c3b0FD47sTWIjDZ8SqUgqcCmXw0Ul/HnBGv44fzUNaifw2NX9GNG3tYZPiQRABS5hW7ppD2OnprNiax4j+rbm3u/0pKmGT4kERgUux3XocOnwqaf/vpYWDWrz9PWpnNuzZdCxRKo8Fbh8o0/W7CRtWjobdh7ke4OTSLuoOw1ra/iUSCxQgUuZ9uUX8vDsFfzt8420b1qXV344mNM7afiUSCxRgct/mbd8G3dOzyQ3L5/RZ3bkV+d2pU5NXQYvEmtU4PIvO/cXcN9bWcxc+jXdWjbgyesG0K9d46BjicgxqMAFd2fm0q+5760s8vIL+dW5XfnxsE7UrKHL4EVimQq8isvZe4i7pmcyb0Uufds15pFRKXRr1SDoWCISBhV4FVVS4rz6xSYenr2cwpIS7rqkBzcN7UB1XQYvEjdU4FXQ+h0HSJuWzqdrd3F6p6ZMuDyFpKZ1g44lIuWkAq9CiopLePbjdfxu7ipqVq/GhMv7cNXAdroMXiROqcCriBVb9zF2SjpLN+/l3B4teeCy3rRqVDvoWCJyEsK5I0874EWgJaU3b5js7o+ZWRPgNSCZ0hs6fLesmxpLsAqKivnT/DU8MT+bRnUSePyaUxmekqi9bpFKIJw98CJK7zj/pZk1ABab2XvAjcA8d59gZmlAGjA2elGlvL7auJuxU9NZtW0/I09tw93De9KkXs2gY4lIhIRzR54cICf0OM/MlgNtgEuBYaHFXgAWoAKPCQcPF/G7uat49uN1tGpYm+duHMhZ3VsEHUtEIqxcx8DNLBk4FfgMaHnETY23UnqIpayvGQ2MBkhKSjrRnBKmf2TvIG1aBht3HeTaIUmMvbA7DTR8SqRSCrvAzaw+MBX4pbvvO/IYqru7mXlZX+fuk4HJAKmpqWUuIydv76FCHp69nFe/2ESHZvV4dfQQhnRsGnQsEYmisArczBIoLe+X3X1a6OVtZpbo7jlmlgjkRiukfLO5y7Zy15uZ7NhfwI++XTp8qnaChk+JVHbhnIViwDPAcnf//RFvzQRuACaEfp8RlYRyTDv2FzB+5jJmpefQvVUDnr4hlZS2Gj4lUlWEswc+FLgOyDCzJaHX7qC0uF83s5uBDcB3oxNRjubuvLlkC/e9lcXBgmJuO68rtw7rREJ1DZ8SqUrCOQvlI+BYJw2fE9k4cjxf7znEndMzmL9yO/2TGjNxVApdWmr4lEhVpCsx40RJifPy5xuZMHs5JQ73fqcn15+WrOFTIlWYCjwOrN2+n7SpGXy+fhdndG7Gw5f3oV0TDZ8SqepU4DGsqLiEpz9ax6T3VlGrRjUeuSKFKwe01WXwIgKowGNW1tf7GDN1KZlb9nFBr5bcf2lvWjTU8CkR+TcVeIwpKCrmjx9k8+cFa2hcN4Envt+fi3q30l63iPwXFXgMWbxhF2OmpLNm+wFG9W/L3cN70Liuhk+JSNlU4DHgQEER//vuSl74ZD2tG9XhhR8M4ttdmwcdS0RinAo8YH9fvZ1x0zLYvPsQN5zWntsv7E79WvrfIiLHp6YIyN6DhTzwdhZvLN5Mx+b1eOPW0xiY3CToWCISR1TgAZiTmcPdM5ax68BhfjKsEz8/p4uGT4lIuanAK1BuXj73zljGO5lb6ZnYkOduHEjvNo2CjiUicUoFXgHcnalfbuH+WVkcKizm9gu6MfrMjho+JSInRQUeZZt3H+SO6ZksXLWd1PanMGFUCp1b1A86lohUAirwKCkpcV76dAMT56wA4L4RvbhuSHuqafiUiESICjwKsnP3kzY1nUUbdnNm1+Y8NLI3bU/R8CkRiSwVeAQVFpcweeFaHnt/NXVqVud3V/bl8v5tdBm8iERFOLdUexYYDuS6e+/Qa+OBHwLbQ4vd4e6zoxUyHmRu2cuYKelk5ezj4j6tuG9Eb5o3qBV0LBGpxMLZA38e+CPw4lGvT3L3RyOeKM7kFxbz2LzVTF64lib1avLktf25sHdi0LFEpAoI55ZqC80sOfpR4s8X63cxdko6a3cc4MoBbbnrkp40qpsQdCwRqSJO5hj4T83semARcJu77y5rITMbDYwGSEpKOonVxY79BUU8MmcFL36ygban1OGlmwfxrS4aPiUiFetEryT5M9AJ6AfkAL871oLuPtndU909tXnz+C+5BStzuWDSQl76dAM3DU3m3V+eqfIWkUCc0B64u2/752MzewqYFbFEMWr3gcPc/3YW077cQucW9Zly6+kMaH9K0LFEpAo7oQI3s0R3zwk9HQlkRi5SbHF33sncyj0zMtlzsJCfnd2Zn57dmVo1NHxKRIIVzmmEfwOGAc3MbDNwLzDMzPoBDqwHfhTFjIHJ3ZfP3TMyeXfZNvq0acSLPxhMz9YNg44lIgKEdxbKNWW8/EwUssQMd+eNRZt54O0sCopKSLuoO7ec0YEaGj4lIjFEV2IeZdOug4yblsFH2TsYlNyECaP60LG5hk+JSOxRgYcUlzgv/GM9//vuSqpXM+6/rDffH5Sk4VMiErNU4MDqbXmMnZrOlxv3MKxbcx4a2YfWjesEHUtE5BtV6QI/XFTCXz5cw+MfZFOvVnX+cFU/Lu3XWsOnRCQuVNkCT9+8hzFT0lmxNY/hKYmMH9GLZvU1fEpE4keVK/D8wmImvbeKp/6+lmb1azH5ugGc36tV0LFERMqtShX4p2t3kjY1nfU7D3LNoHakXdSDRnU0fEpE4lOVKPC8/EImvLOClz/bSFKTurxyy2BO79ws6FgiIiel0hf4Byu2cef0TLbty+eWMzrw6/O7Urdmpf/PFpEqoNI22a4Dh/ntW8t4c8nXdGlRnyd+fDqnJmn4lIhUHpWuwN2dt9JzGD9zGfsOFfKLc7rwk7M6afiUiFQ6larAt+7N5643M3l/+TZS2jbikR8OpnsrDZ8SkcqpUhS4u/PqF5t46O3lFJaUcOfFPbhpaLKGT4lIpRb3Bb5h5wHSpmbwydqdDOnYhAmXp5DcrF7QsUREoi5uC7y4xHnu43U8OnclCdWq8dDIPlw9sJ2GT4lIlRGXBb5yax5jpqazdNMezuneggdG9iaxkYZPiUjVEs4deZ4FhgO57t479FoT4DUgmdI78nz3WHelj6TDRSU8sSCbP83PpkHtBB67uh8j+mr4lIhUTeF8yvc8cOFRr6UB89y9CzAv9Dyqlmzaw3ce/4g/vL+ai/sk8t6vzuTSfm1U3iJSZYVzS7WFZpZ81MuXUnqfTIAXgAXA2Ajm+g+Pz1vNpPdX0aJBbZ65IZVzerSM1qpEROLGiR4Db3nEXem3AsdsVDMbDYwGSEpKOqGVJTWty9WDkki7qDsNa2v4lIgIgLn78Rcq3QOfdcQx8D3u3viI93e7+3GvU09NTfVFixadeFoRkSrIzBa7e+rRr5/olS7bzCwx9AcnArknE05ERMrvRAt8JnBD6PENwIzIxBERkXAdt8DN7G/AJ0A3M9tsZjcDE4DzzGw1cG7ouYiIVKBwzkK55hhvnRPhLCIiUg6a9iQiEqdU4CIicUoFLiISp1TgIiJxKqwLeSK2MrPtwIYT/PJmwI4IxokU5Sof5Sof5SqfWM0FJ5etvbs3P/rFCi3wk/BVqjYAAAQ3SURBVGFmi8q6EiloylU+ylU+ylU+sZoLopNNh1BEROKUClxEJE7FU4FPDjrAMShX+ShX+ShX+cRqLohCtrg5Bi4iIv8pnvbARUTkCCpwEZE4FVMFbmbPmlmumWUe430zs/8zs2wzSzez/jGSa5iZ7TWzJaFf91RQrnZmNt/MssxsmZn9ooxlKnybhZmrwreZmdU2s8/NbGko131lLFPLzF4Lba/PyridYFC5bjSz7Udsr1uineuIdVc3s6/MbFYZ71X49gozVyDby8zWm1lGaJ3/dfeaiH8/unvM/ALOBPoDmcd4/2LgHcCAIcBnMZJrGKV3LKro7ZUI9A89bgCsAnoGvc3CzFXh2yy0DeqHHicAnwFDjlrmJ8CTocdXA6/FSK4bgT9W9N+x0Lp/DbxS1v+vILZXmLkC2V7AeqDZN7wf0e/HmNoDd/eFwK5vWORS4EUv9SnQ+J93Bgo4VyDcPcfdvww9zgOWA22OWqzCt1mYuSpcaBvsDz1NCP06+lP8Sym9UTfAFOAcM7MYyBUIM2sLXAI8fYxFKnx7hZkrVkX0+zGmCjwMbYBNRzzfTAwUQ8hpoR+B3zGzXhW98tCPrqdSuvd2pEC32TfkggC2WejH7iWU3gbwPXc/5vZy9yJgL9A0BnIBjAr92D3FzNpFO1PIH4AxQMkx3g9ke4WRC4LZXg7MNbPFVnpD96NF9Psx3go8Vn1J6ayCvsDjwJsVuXIzqw9MBX7p7vsqct3f5Di5Atlm7l7s7v2AtsAgM+tdEes9njByvQUku3sK8B7/3uuNGjMbDuS6++Jor6s8wsxV4dsr5Ax37w9cBPyPmZ0ZzZXFW4FvAY78l7Rt6LVAufu+f/4I7O6zgQQza1YR6zazBEpL8mV3n1bGIoFss+PlCnKbhda5B5gPXHjUW//aXmZWA2gE7Aw6l7vvdPeC0NOngQEVEGcoMMLM1gOvAmeb2V+PWiaI7XXcXAFtL9x9S+j3XGA6MOioRSL6/RhvBT4TuD70Se4QYK+75wQdysxa/fO4n5kNonS7Rv2bPrTOZ4Dl7v77YyxW4dssnFxBbDMza25mjUOP6wDnASuOWuzIG3ZfAXzgoU+fgsx11HHSEZR+rhBV7j7O3du6ezKlH1B+4O7XHrVYhW+vcHIFsb3MrJ6ZNfjnY+B84Ogz1yL6/Xjce2JWJCu9gfIwoJmZbQbupfQDHdz9SWA2pZ/iZgMHgZtiJNcVwI/NrAg4BFwd7b/EIUOB64CM0PFTgDuApCOyBbHNwskVxDZLBF4ws+qU/oPxurvPMrPfAovcfSal//C8ZGbZlH5wfXWUM4Wb6+dmNgIoCuW6sQJylSkGtlc4uYLYXi2B6aH9khrAK+4+x8xuheh8P+pSehGROBVvh1BERCREBS4iEqdU4CIicUoFLiISp1TgIiJxSgUuIhKnVOAiInHq/wEL+izIhRiLOwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}