r"""Query the source code of a given Python module.

r"""Get or create a Python module for the given source code.

r"""Get or create a Python module for the given source code.

r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.

r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.

Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.

Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.

:param img: numpy array rgb :return: PIL Image

Apply dynamic loss scaling for the given expression.

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

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

Finds the python object with the given name and calls it as a function.

Finds the python object with the given name and calls it as a function.

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

Not quite sure why there is a factor of 4 here

Convert legacy network pickle into the native PyTorch format. The tool is able to load the main network configurations exported using the TensorF

Create tf.Session based on config dict.

Download a file if it does not already exist.

Given a face_w, optimize a body_w with suitable body pose & shape for face_w

Create the necessary ops to include autosummaries in TensorBoard report. Note: This should be done only once per graph.

Generate images using pretrained network pickle. Examples: \b # Generate an image using pre-trained AFHQv2 model ("Ours" in Figure 1, le

Finds the python object with the given name.

Finds the python object with the given name.

Construct TensorFlow expression(s) for the output(s) of this network, given the input expression(s).

Convert mask from gr.Image(0 to 255, RGBA) to binary mask.

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Determine whether the given object is a valid URL string.

Determine whether the given object is a valid URL string.

Returns a list of (layer_name, output_expr, trainable_vars) tuples corresponding to individual layers of the network. Mainly intended to be us

Interactive model visualizer. Optional PATH argument can be used specify which .pkl file to load.