class_docstr: ArrowIOResource holds resources for ArrowIOTensor
docstr: Initialize the resource op with a pyarrow.Table
docstr_tok: ["Initialize","the","resource","op","with","a","pyarrow.Table"]
code: def init_resource_op(self, table):
      """Initialize the resource op with a pyarrow.Table"""
      with tf.name_scope("ArrowIOTensor") as scope:
          # Serialize the schema to send to the kernel
          schema_buffer = table.schema.serialize()
  
          # References to prevent data from being freed until op is evaluated
          self.arrow_data_refs = [table, schema_buffer]
  
          # Get buffer addresses as long ints
          schema_buffer_addr = schema_buffer.address
          schema_buffer_size = schema_buffer.size
          array_tuple = _extract_table_arrays(table)
          (
              array_buffer_addrs,
              array_buffer_sizes,
              array_lengths,
          ) = array_tuple
  
          # Create the Arrow readable resource op
          resource_op = core_ops.io_arrow_readable_from_memory_init(
              schema_buffer_addr,
              schema_buffer_size,
              array_buffer_addrs,
              array_buffer_sizes,
              array_lengths,
              container=scope,
              shared_name="pyarrow.Table{}/{}".format(
                  table.schema.names, uuid.uuid4().hex
              ),
          )
  
          return resource_op
  
imports: import uuid
  import tensorflow
  from tensorflow_io.python.ops import io_tensor_ops
  from tensorflow_io.python.ops import core_ops
label_desc: Use image node_id 4 for calling the ArrowIOResource obj's underlying member method code with example usage: obj.init_resource_op(table) and returns: resource_op