parameters: (
          self,
          serialized_batches,
          columns,
          output_types,
          output_shapes=None,
          batch_size=None,
          batch_mode="keep_remainder",
          arrow_buffer=None,
      )
docstring: Create an ArrowDataset from a Tensor of serialized batches.
          This constructor requires pyarrow to be installed.
  
          Args:
              serialized_batches: A string Tensor as a serialized buffer containing
                                  Arrow record batches in Arrow File format
              columns: A list of column indices to be used in the Dataset
              output_types: Tensor dtypes of the output tensors
              output_shapes: TensorShapes of the output tensors or None to
                          infer partial
              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)
              arrow_buffer: Optional Arrow Buffer containing Arrow record batches in
                          Arrow File format. This will share the Arrow buffer with
                          the C++ kernel by address for zero-copy. Only supported if
                          the kernel process is local, with TensorFlow in eager mode.
                          If this is used, set `serialized_batches` to `None`.
docstring_summary: Create an ArrowDataset from a Tensor of serialized batches.
          This constructor requires pyarrow to be installed.
docstring_tokens: Create an ArrowDataset from a Tensor of serialized batches . This constructor requires pyarrow to be installed .
function: def __init__(
          self,
          serialized_batches,
          columns,
          output_types,
          output_shapes=None,
          batch_size=None,
          batch_mode="keep_remainder",
          arrow_buffer=None,
      ):
          """Create an ArrowDataset from a Tensor of serialized batches.
          This constructor requires pyarrow to be installed.
  
          Args:
              serialized_batches: A string Tensor as a serialized buffer containing
                                  Arrow record batches in Arrow File format
              columns: A list of column indices to be used in the Dataset
              output_types: Tensor dtypes of the output tensors
              output_shapes: TensorShapes of the output tensors or None to
                          infer partial
              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)
              arrow_buffer: Optional Arrow Buffer containing Arrow record batches in
                          Arrow File format. This will share the Arrow buffer with
                          the C++ kernel by address for zero-copy. Only supported if
                          the kernel process is local, with TensorFlow in eager mode.
                          If this is used, set `serialized_batches` to `None`.
          """
          if serialized_batches is not None:
              make_variant_fn = partial(
                  core_ops.io_arrow_serialized_dataset, serialized_batches
              )
          elif arrow_buffer is None:
              raise ValueError("Must set either serialzied_batches or arrow_buffer")
          elif not tf.executing_eagerly():
              raise ValueError(
                  "Using arrow_buffer for zero-copy only supported in "
                  "TensorFlow Eager mode."
              )
          else:
              address_int = arrow_buffer.address
              buffer_address = tf.convert_to_tensor(
                  address_int, dtype=dtypes.uint64, name="buffer_address"
              )
              buffer_size = tf.convert_to_tensor(
                  arrow_buffer.size, dtype=dtypes.int64, name="buffer_size"
              )
              make_variant_fn = partial(
                  core_ops.io_arrow_zero_copy_dataset, buffer_address, buffer_size
              )
              # Keep a reference to the arrow buffers used
              self._arrow_buffer_refs = [arrow_buffer]
          super().__init__(
              make_variant_fn,
              columns,
              output_types,
              output_shapes,
              batch_size,
              batch_mode,
          )
function_tokens: def __init__ ( self , serialized_batches , columns , output_types , output_shapes = None , batch_size = None , batch_mode = "keep_remainder" , arrow_buffer = None , ) : if serialized_batches is not None : make_variant_fn = partial ( core_ops . io_arrow_serialized_dataset , serialized_batches ) elif arrow_buffer is None : raise ValueError ( "Must set either serialzied_batches or arrow_buffer" ) elif not tf . executing_eagerly ( ) : raise ValueError ( "Using arrow_buffer for zero-copy only supported in " "TensorFlow Eager mode." ) else : address_int = arrow_buffer . address buffer_address = tf . convert_to_tensor ( address_int , dtype = dtypes . uint64 , name = "buffer_address" ) buffer_size = tf . convert_to_tensor ( arrow_buffer . size , dtype = dtypes . int64 , name = "buffer_size" ) make_variant_fn = partial ( core_ops . io_arrow_zero_copy_dataset , buffer_address , buffer_size ) # Keep a reference to the arrow buffers used self . _arrow_buffer_refs = [ arrow_buffer ] super ( ) . __init__ ( make_variant_fn , columns , output_types , output_shapes , batch_size , batch_mode , )