class_docstr: Convolution + upsampling network module.
class_docstr_tok: ["Convolution","+","upsampling","network","module","."]
docstr: Initialize ConvInUpsampleNetwork module.
  
  Args:
      upsample_scales (list): List of upsampling scales.
      nonlinear_activation (Optional[str]): Activation function name.
      nonlinear_activation_params (Dict[str, Any]): Arguments for the specified
          activation function.
      mode (str): Interpolation mode.
      freq_axis_kernel_size (int): Kernel size in the direction of
          frequency axis.
      aux_channels (int): Number of channels of pre-conv layer.
      aux_context_window (int): Context window size of the pre-conv layer.
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code: def __init__(
      self,
      upsample_scales: List[int],
      nonlinear_activation: Optional[str] = None,
      nonlinear_activation_params: Dict[str, Any] = {},
      interpolate_mode: str = "nearest",
      freq_axis_kernel_size: int = 1,
      aux_channels: int = 80,
      aux_context_window: int = 0,
  ):
      """Initialize ConvInUpsampleNetwork module.
  
      Args:
          upsample_scales (list): List of upsampling scales.
          nonlinear_activation (Optional[str]): Activation function name.
          nonlinear_activation_params (Dict[str, Any]): Arguments for the specified
              activation function.
          mode (str): Interpolation mode.
          freq_axis_kernel_size (int): Kernel size in the direction of
              frequency axis.
          aux_channels (int): Number of channels of pre-conv layer.
          aux_context_window (int): Context window size of the pre-conv layer.
  
      """
      super().__init__()
      self.aux_context_window = aux_context_window
      # To capture wide-context information in conditional features
      kernel_size = 2 * aux_context_window + 1
      # NOTE(kan-bayashi): Use pad here, which is not used in parallel_wavegan
      self.pad = torch.nn.ReplicationPad1d(aux_context_window)
      self.conv_in = Conv1d(
          aux_channels,
          aux_channels,
          kernel_size=kernel_size,
          bias=False,
      )
      self.upsample = UpsampleNetwork(
          upsample_scales=upsample_scales,
          nonlinear_activation=nonlinear_activation,
          nonlinear_activation_params=nonlinear_activation_params,
          interpolate_mode=interpolate_mode,
          freq_axis_kernel_size=freq_axis_kernel_size,
      )
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_tts.wavenet.residual_block import Conv1d
label_desc: Use image node_id 1 to create a new ConvInUpsampleNetwork object from inherited base classes: torch.nn with example: obj = ConvInUpsampleNetwork(upsample_scales, nonlinear_activation, nonlinear_activation_params, interpolate_mode, freq_axis_kernel_size, aux_channels, aux_context_window)