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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def read_csv_with_errors(cls, url, delimiter=',', header=True, comment_char='', escape_char='\\', double_quote=True, quote_char='\"', skip_initial_space=True, column_type_hints=None, na_values=["NA"], line_terminator='\n', usecols = [], nrows=None, skiprows=0, verbose=True, nrows_to_infer=100, true_values=[], false_values=[], _only_raw_string_substitutions=False, **kwargs):
 """ Constructs an SFrame from a CSV file or a path to multiple CSVs, and returns a pair containing the SFrame and a dict of filenames to SArrays indicating for each file, what are the incorrectly parsed lines encountered. Parameters url : string Location of the CSV file or directory to load. If URL is a directory or a "glob" pattern, all matching files will be loaded. delimiter : string, optional This describes the delimiter used for parsing csv files. header : bool, optional If true, uses the first row as the column names. Otherwise use the comment_char : string, optional The character which denotes that the remainder of the line is a comment. escape_char : string, optional Character which begins a C escape sequence. Defaults to backslash(\\) Set to None to disable. double_quote : bool, optional If True, two consecutive quotes in a string are parsed to a single quote. quote_char : string, optional Character sequence that indicates a quote. skip_initial_space : bool, optional Ignore extra spaces at the start of a field column_type_hints : None, type, list[type], dict[string, type], optional This provides type hints for each column. By default, this method attempts to detect the type of each column automatically. Supported types are int, float, str, list, dict, and array.array. * If a single type is provided, the type will be applied to all columns. For instance, column_type_hints=float will force all columns to be parsed as float. * If a list of types is provided, the types applies to each column in order, e.g.[int, float, str] will parse the first column as int, second as float and third as string. * If a dictionary of column name to type is provided, each type value in the dictionary is applied to the key it belongs to. For instance {'user':int} will hint that the column called "user" should be parsed as an integer, and the rest will be type inferred. na_values : str | list of str, optional A string or list of strings to be interpreted as missing values. true_values : str | list of str, optional A string or list of strings to be interpreted as 1 false_values : str | list of str, optional A string or list of strings to be interpreted as 0 line_terminator : str, optional A string to be interpreted as the line terminator. Defaults to "\\n" which will also correctly match Mac, Linux and Windows line endings ("\\r", "\\n" and "\\r\\n" respectively) usecols : list of str, optional A subset of column names to output. If unspecified (default), all columns will be read. This can provide performance gains if the number of columns are large. If the input file has no headers, usecols=['X1','X3'] will read columns 1 and 3. nrows : int, optional If set, only this many rows will be read from the file. skiprows : int, optional If set, this number of rows at the start of the file are skipped. verbose : bool, optional If True, print the progress. Returns ------- out : tuple The first element is the SFrame with good data. The second element is a dictionary of filenames to SArrays indicating for each file, what are the incorrectly parsed lines encountered. See Also -------- read_csv, SFrame Examples -------- | user_id | movie_id | rating | | 25904 | 1663 | 3 | | 25907 | 1663 | 3 | | 25923 | 1663 | 3 | | 25924 | 1663 | 3 | | 25928 | 1663 | 2 | [98 rows x 3 columns] {'https://static.turi.com/datasets/bad_csv_example.csv': dtype: str Rows: 1 ['x,y,z,a,b,c']} """

        return cls._read_csv_impl(url,
                                  delimiter=delimiter,
                                  header=header,
                                  error_bad_lines=False, # we are storing errors,
                                                         # thus we must not fail
                                                         # on bad lines
                                  comment_char=comment_char,
                                  escape_char=escape_char,
                                  double_quote=double_quote,
                                  quote_char=quote_char,
                                  skip_initial_space=skip_initial_space,
                                  column_type_hints=column_type_hints,
                                  na_values=na_values,
                                  line_terminator=line_terminator,
                                  usecols=usecols,
                                  nrows=nrows,
                                  verbose=verbose,
                                  skiprows=skiprows,
                                  store_errors=True,
                                  nrows_to_infer=nrows_to_infer,
                                  true_values=true_values,
                                  false_values=false_values,
                                  _only_raw_string_substitutions=_only_raw_string_substitutions,
                                  **kwargs)