docstr: Create the StringIndex from a dict of indices.
  
  :param indices: dict, the key is the categorical column, and the value is the
         corresponding index. We assume that the key is a str and the value
         is a int.
  :param col_name: str. The column name of the categorical column.
  
  :return: A StringIndex.
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code: def from_dict(
      cls, indices: Dict[str, int], col_name: str
  ) -> "StringIndex":
      """
      Create the StringIndex from a dict of indices.
  
      :param indices: dict, the key is the categorical column, and the value is the
             corresponding index. We assume that the key is a str and the value
             is a int.
      :param col_name: str. The column name of the categorical column.
  
      :return: A StringIndex.
      """
      spark = OrcaContext.get_spark_session()
      if not isinstance(indices, dict):
          invalidInputError(
              False,
              "indices should be dict, but get "
              + indices.__class__.__name__,
          )
      if not col_name:
          invalidInputError(
              False, "col_name should be str, but get None"
          )
      if not isinstance(col_name, str):
          invalidInputError(
              False,
              "col_name should be str, but get "
              + col_name.__class__.__name__,
          )
      new_indices = map(
          lambda x: {col_name: x[0], "id": x[1]}, indices.items()
      )
      schema = StructType(
          [
              StructField(col_name, StringType(), False),
              StructField("id", IntegerType(), False),
          ]
      )
      df = spark.createDataFrame(
          (Row(**x) for x in new_indices), schema=schema
      )
      return cls(df, col_name)
  
imports: import pyspark.sql.functions
  from pyspark.sql import Row
  from pyspark.sql.types import IntegerType, StructType, StringType, StructField
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca.data import SparkXShards
  from bigdl.orca import OrcaContext
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.feature import StandardScaler
  from pyspark.ml.feature import VectorAssembler
  from pyspark.ml import Pipeline
  from bigdl.orca.data.utils import *
  import uuid
  from typing import TYPE_CHECKING, Tuple, Union, List
label_desc: Use image node_id 3 for calling the StringIndex obj's underlying member method code with example usage: obj.from_dict(cls, indices, col_name) and returns: cls