Add formatting to Code Generation

[gsheni]

• As a developer, I want to write code that is properly formatted, and indented when using Code Generation. I want the pipeline parameters to be indented, and spaced.

Current

from evalml.pipelines.components import (
        Imputer,
        OneHotEncoder,
        RandomForestClassifier
)
from evalml.pipelines.multiclass_classification_pipeline import MulticlassClassificationPipeline

class CustomPipeline(MulticlassClassificationPipeline):
        component_graph = ['Imputer', MyDropNullColumns, 'One Hot Encoder', 'Random Forest Classifier']
        custom_hyperparameters = {'Imputer': {'numeric_impute_strategy': ['mean', 'median']}}
        name = 'Custom Pipeline'

parameters = {'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'median', 'categorical_fill_value': None, 'numeric_fill_value': None}, 'My Drop Null Columns Transformer': {'pct_null_threshold': 1.0}, 'One Hot Encoder': {'top_n': 10, 'features_to_encode': None, 'categories': None, 'drop': None, 'handle_unknown': 'ignore', 'handle_missing': 'error'}, 'Random Forest Classifier': {'n_estimators': 100, 'max_depth': 6, 'n_jobs': -1}}
pipeline = CustomPipeline(parameters)

Proposed

from evalml.pipelines.components import (
        Imputer,
        OneHotEncoder,
        RandomForestClassifier
)
from evalml.pipelines.multiclass_classification_pipeline import MulticlassClassificationPipeline,


class CustomPipeline(MulticlassClassificationPipeline):
    component_graph = [
        "Imputer",
        MyDropNullColumns,
        "One Hot Encoder",
        "Random Forest Classifier",
    ]
    custom_hyperparameters = {
        "Imputer": {"numeric_impute_strategy": ["mean", "median"]}
    }
    name = "Custom Pipeline"


parameters = {
    "Imputer": {
        "categorical_impute_strategy": "most_frequent",
        "numeric_impute_strategy": "median",
        "categorical_fill_value": None,
        "numeric_fill_value": None,
    },
    "My Drop Null Columns Transformer": {"pct_null_threshold": 1.0},
    "One Hot Encoder": {
        "top_n": 10,
        "features_to_encode": None,
        "categories": None,
        "drop": None,
        "handle_unknown": "ignore",
        "handle_missing": "error",
    },
    "Random Forest Classifier": {"n_estimators": 100, "max_depth": 6, "n_jobs": -1},
}
pipeline = CustomPipeline(parameters)