code: def analyze(dframe):
      """Analyzes a Spark DataFrame for the tensor content, and returns a new dataframe with extra metadata that
       describes the numerical shape of the content.
  
       This method is useful when a dataframe contains non-scalar tensors, for which the shape must be checked beforehand.
  
       Note: nullable fields are not accepted.
  
       The function [print_schema] lets users introspect the information added to the DataFrame.
  
      :param dframe: a Spark DataFrame
      :return: a Spark DataFrame with metadata information embedded.
      """
      return DataFrame(_java_api().analyze(dframe._jdf), _sql)
  
imports: import tensorflow
  import numpy
  import logging
  import tempfile
  import pandas
  from pyspark import RDD, SparkContext
  from pyspark.sql import SQLContext, Row, DataFrame
  from pyspark.sql.types import DoubleType, IntegerType, LongType, FloatType, ArrayType
label_desc: Use image node_id 19 for calling a global function with example usage: analyze(dframe) and returns: DataFrame