r"""Returns the mean of the scalars that were accumulated for the given statistic between the last two calls to `update()`, or NaN if

Flush written text to both stdout and a file, if open.

Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.

r"""Copies current values of the internal counters to the user-visible state and resets them for the next round. If `keep_previous=Tr

Write text to stdout (and a file) and optionally flush.

Order the data so it range like this: input [[0, 2, 4, 6], [1, 3, 5, 7]] -> output [0, 1, 2, 3, 4, 5, ...]

r"""Returns the raw moments that were accumulated for the given statistic between the last two calls to `update()`, or zero if no scal

r"""Returns the averages accumulated between the last two calls to `update()` as an `dnnlib.EasyDict`. The contents are as follows:

Traverses the object name and returns the last (rightmost) python object.

Args: data: any, the data needs to be broadcasted

r"""Pad, upsample, filter, and downsample a batch of 2D images. Performs the following sequence of operations for each channel: 1. Upsample

Fast CUDA implementation of `upfirdn2d()` using custom ops.

Flush, close possible files, and remove stdout/stderr mirroring.

Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part r

r"""Test whether the given object or class is persistent, i.e., whether it will save its source code when pickled.

Args: x : Input tensor of shape [batch_size, in_channels, in_height, in_width]. weight : Weight tensor of

r"""Returns the names of all statistics broadcasted so far that match the regular expression specified at construction time.

Fast CUDA implementation of `bias_act()` using custom ops.

Slow reference implementation of `bias_act()` using standard TensorFlow ops.

r"""Check that the given object is pickleable, raising an exception if it is not. This function is expected to be considerably more efficient