class_docstr: An Arrow Dataset for reading record batches from Arrow feather files.
  Feather is a light-weight columnar format ideal for simple writing of
  Pandas DataFrames. Pyarrow can be used for reading/writing Feather files,
  see https://arrow.apache.org/docs/python/ipc.html#feather-format
docstr: Create an Arrow Dataset for reading record batches from Arrow feather
  files, inferring output types and shapes from the given Arrow schema.
  This method requires pyarrow to be installed.
  
  Args:
      filenames: A `tf.string` tensor, Python list or scalar containing files
                  in Arrow Feather format
      schema: Arrow schema defining the record batch data in the stream
      columns: A list of column indicies to use from the schema, None for all
      batch_size: Batch size of output tensors, setting a batch size here
                  will create batched tensors from Arrow memory and can be more
                  efficient than using tf.data.Dataset.batch().
                  NOTE: batch_size does not need to be set if batch_mode='auto'
      batch_mode: Mode of batching, supported strings:
                  "keep_remainder" (default, keeps partial batch data),
                  "drop_remainder" (discard partial batch data),
                  "auto" (size to number of records in Arrow record batch)
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code: def from_schema(
      cls,
      filenames,
      schema,
      columns=None,
      batch_size=None,
      batch_mode="keep_remainder",
  ):
      """Create an Arrow Dataset for reading record batches from Arrow feather
      files, inferring output types and shapes from the given Arrow schema.
      This method requires pyarrow to be installed.
  
      Args:
          filenames: A `tf.string` tensor, Python list or scalar containing files
                      in Arrow Feather format
          schema: Arrow schema defining the record batch data in the stream
          columns: A list of column indicies to use from the schema, None for all
          batch_size: Batch size of output tensors, setting a batch size here
                      will create batched tensors from Arrow memory and can be more
                      efficient than using tf.data.Dataset.batch().
                      NOTE: batch_size does not need to be set if batch_mode='auto'
          batch_mode: Mode of batching, supported strings:
                      "keep_remainder" (default, keeps partial batch data),
                      "drop_remainder" (discard partial batch data),
                      "auto" (size to number of records in Arrow record batch)
      """
      if columns is None:
          columns = list(range(len(schema)))
      output_types, output_shapes = arrow_schema_to_tensor_types(schema)
      return cls(
          filenames,
          columns,
          output_types,
          output_shapes,
          batch_size,
          batch_mode,
      )
  
imports: from functools import partial
  import io
  from itertools import chain
  import os
  import socket
  import threading
  import tempfile
  import tensorflow
  from tensorflow import dtypes
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import structure
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 2 for calling the ArrowFeatherDataset obj's underlying member method code with example usage: obj.from_schema(cls, filenames, schema, columns, batch_size, batch_mode) and returns: cls