handling. RequestHandler.write(chunk: str | bytes | dict) → None Writes the given chunk to the output buffer. To write the output to the network, use the flush() method below. If the given chunk is a dictionary RequestHandler.flush(include_footers: bool = False) → Future[None] Flushes the current output buffer to the network. Changed in version 4.0: Now returns a Future if no callback is given. Changed in t: float | None = None, body_timeout: float | None = None, max_body_size: int | None = None, max_buffer_size: int | None = None, trusted_downstream: List[str] | None = None) A non-blocking, single-threaded

None [https://docs.python.org/3/library/constants.html#None] Writes the given chunk to the output buffer. To write the output to the network, use the flush() method below. If the given chunk is a dictionary Future[None [https://docs.python.org/3/library/constants.html#None]] Flushes the current output buffer to the network. Changed in version 4.0: Now returns a Future if no callback is given. Changed in org/3/library/functions.html#int] | None [https://docs.python.org/3/library/constants.html#None] = None, max_buffer_size: int [https://docs.python.org/3/library/functions.html#int] | None [https://docs.python.o

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race-free. Here sum_single is reused, with its own internal buffer s, and vector a is split into nthreads() chunks for parallel work via nthreads() @spawn-ed tasks nthreads()) because concurrent tasks can yield, meaning multiple concurrent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race-free. Here sum_single is reused, with its own internal buffer s, and vector a is split into nthreads() chunks for parallel work via nthreads() @spawn-ed tasks nthreads()) because concurrent tasks can yield, meaning multiple concurrent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into nthreads() chunks for parallel work. We then use Threads.@spawn nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into nthreads() chunks for parallel work. We then use Threads.@spawn nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into nthreads() chunks for parallel work. We then use Threads.@spawn nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into at most nthreads() chunks for parallel work. We then use Threads nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into at most nthreads() chunks for parallel work. We then use Threads nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available

following method to print the object to a given output object io (representing a file, terminal, buffer, etcetera; see Networking and Streams): julia> Base.show(io::IO, z::Polar) = print(io, z.r, " * segment the sum into chunks that are race- free. Here sum_single is reused, with its own internal buffer s. The input vector a is split into at most nthreads() chunks for parallel work. We then use Threads nthreads()) because concurrent tasks can yield, meaning multiple concur- rent tasks may use the same buffer on a given thread, introducing risk of data races. Further, when more than one thread is available