class_docstr: Decodes CSV records into Arrow RecordBatches.
  
  DEPRECATED: please use tfx_bsl.public.CsvTFXIO instead.
docstr: Initializes the CSV decoder.
  
  Args:
    column_names: List of feature names. Order must match the order in the CSV
      file.
    delimiter: A one-character string used to separate fields.
    skip_blank_lines: A boolean to indicate whether to skip over blank lines
      rather than interpreting them as missing values.
    schema: An optional schema of the input data. If provided, types
      will be inferred from the schema. If this is provided, the feature names
      must equal column_names.
    desired_batch_size: Batch size. The output Arrow RecordBatches will have
      as many rows as the `desired_batch_size`.
    multivalent_columns: Name of column that can contain multiple
      values.
    secondary_delimiter: Delimiter used for parsing multivalent columns.
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code: def __init__(
      self,
      column_names: List[types.FeatureName],
      delimiter: Text = ",",
      skip_blank_lines: bool = True,
      schema: Optional[schema_pb2.Schema] = None,
      desired_batch_size: Optional[
          int
      ] = constants.DEFAULT_DESIRED_INPUT_BATCH_SIZE,
      multivalent_columns: Optional[List[types.FeatureName]] = None,
      secondary_delimiter: Optional[Union[Text, bytes]] = None,
  ):
      """Initializes the CSV decoder.
  
      Args:
        column_names: List of feature names. Order must match the order in the CSV
          file.
        delimiter: A one-character string used to separate fields.
        skip_blank_lines: A boolean to indicate whether to skip over blank lines
          rather than interpreting them as missing values.
        schema: An optional schema of the input data. If provided, types
          will be inferred from the schema. If this is provided, the feature names
          must equal column_names.
        desired_batch_size: Batch size. The output Arrow RecordBatches will have
          as many rows as the `desired_batch_size`.
        multivalent_columns: Name of column that can contain multiple
          values.
        secondary_delimiter: Delimiter used for parsing multivalent columns.
      """
      if not isinstance(column_names, list):
          raise TypeError(
              "column_names is of type %s, should be a list"
              % type(column_names).__name__
          )
  
      self._column_names = column_names
      self._delimiter = delimiter
      self._skip_blank_lines = skip_blank_lines
      self._schema = schema
      self._desired_batch_size = desired_batch_size
      self._multivalent_columns = multivalent_columns
      self._secondary_delimiter = secondary_delimiter
  
file_path: data-validation/tensorflow_data_validation/coders/csv_decoder.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import List, Optional, Text, Union
  import apache_beam
  import pyarrow
  from tensorflow_data_validation import constants
  from tensorflow_data_validation import types
  from tfx_bsl.coders import csv_decoder
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 1 to create a new DecodeCSV object from inherited base classes: beam with example: obj = DecodeCSV(column_names, delimiter, skip_blank_lines, schema, desired_batch_size, multivalent_columns, secondary_delimiter)