Fix AutoencoderKL docstrings. (#8445)

Fixes #8444 

### Description

Some of the docstrings in AutoencoderKL, Encoder and Decoder within
autoencoderkl.py and some of the errors related to argument "channels"
make reference to "num_channels" instead.

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [X] Non-breaking change (fix or new feature that would not break
existing functionality).
- [X] In-line docstrings updated.
- [X] Documentation updated, tested `make html` command in the `docs/`
folder.

---------

Signed-off-by: Virginia Fernandez <[email protected]>
Co-authored-by: Virginia Fernandez <[email protected]>
Co-authored-by: Eric Kerfoot <[email protected]>

Author: 3 people

monai/networks/nets/autoencoderkl.py

@@ -153,9 +153,9 @@ class Encoder(nn.Module):
         channels: sequence of block output channels.
         out_channels: number of channels in the bottom layer (latent space) of the autoencoder.
         num_res_blocks: number of residual blocks (see _ResBlock) per level.
-        norm_num_groups: number of groups for the GroupNorm layers, num_channels must be divisible by this number.
+        norm_num_groups: number of groups for the GroupNorm layers, channels must be divisible by this number.
         norm_eps: epsilon for the normalization.
-        attention_levels: indicate which level from num_channels contain an attention block.
+        attention_levels: indicate which level from channels contain an attention block.
         with_nonlocal_attn: if True use non-local attention block.
         include_fc: whether to include the final linear layer. Default to True.
         use_combined_linear: whether to use a single linear layer for qkv projection, default to False.
@@ -299,9 +299,9 @@ class Decoder(nn.Module):
         in_channels: number of channels in the bottom layer (latent space) of the autoencoder.
         out_channels: number of output channels.
         num_res_blocks: number of residual blocks (see _ResBlock) per level.
-        norm_num_groups: number of groups for the GroupNorm layers, num_channels must be divisible by this number.
+        norm_num_groups: number of groups for the GroupNorm layers, channels must be divisible by this number.
         norm_eps: epsilon for the normalization.
-        attention_levels: indicate which level from num_channels contain an attention block.
+        attention_levels: indicate which level from channels contain an attention block.
         with_nonlocal_attn: if True use non-local attention block.
         use_convtranspose: if True, use ConvTranspose to upsample feature maps in decoder.
         include_fc: whether to include the final linear layer. Default to True.
@@ -483,7 +483,7 @@ class AutoencoderKL(nn.Module):
         channels: number of output channels for each block.
         attention_levels: sequence of levels to add attention.
         latent_channels: latent embedding dimension.
-        norm_num_groups: number of groups for the GroupNorm layers, num_channels must be divisible by this number.
+        norm_num_groups: number of groups for the GroupNorm layers, channels must be divisible by this number.
         norm_eps: epsilon for the normalization.
         with_encoder_nonlocal_attn: if True use non-local attention block in the encoder.
         with_decoder_nonlocal_attn: if True use non-local attention block in the decoder.
@@ -518,18 +518,18 @@ def __init__(
 
         # All number of channels should be multiple of num_groups
         if any((out_channel % norm_num_groups) != 0 for out_channel in channels):
-            raise ValueError("AutoencoderKL expects all num_channels being multiple of norm_num_groups")
+            raise ValueError("AutoencoderKL expects all channels being multiple of norm_num_groups")
 
         if len(channels) != len(attention_levels):
-            raise ValueError("AutoencoderKL expects num_channels being same size of attention_levels")
+            raise ValueError("AutoencoderKL expects channels being same size of attention_levels")
 
         if isinstance(num_res_blocks, int):
             num_res_blocks = ensure_tuple_rep(num_res_blocks, len(channels))
 
         if len(num_res_blocks) != len(channels):
             raise ValueError(
                 "`num_res_blocks` should be a single integer or a tuple of integers with the same length as "
-                "`num_channels`."
+                "`channels`."
             )
 
         self.encoder: nn.Module = Encoder(