code: def __init__(
      self,
      n_mel_channels,
      n_context_dim,
      final_out_channels,
      n_layers=2,
      kernel_size=5,
      with_dilation=True,
      max_channels=1024,
      zero_init=True,
      use_partial_padding=True,
  ):
      super(SimpleConvNet, self).__init__()
      self.layers = torch.nn.ModuleList()
      self.n_layers = n_layers
      in_channels = n_mel_channels + n_context_dim
      out_channels = -1
      self.use_partial_padding = use_partial_padding
      for i in range(n_layers):
          dilation = 2**i if with_dilation else 1
          padding = int((kernel_size * dilation - dilation) / 2)
          out_channels = min(max_channels, in_channels * 2)
          self.layers.append(
              ConvNorm(
                  in_channels,
                  out_channels,
                  kernel_size=kernel_size,
                  stride=1,
                  padding=padding,
                  dilation=dilation,
                  bias=True,
                  w_init_gain="relu",
                  use_partial_padding=use_partial_padding,
              )
          )
          in_channels = out_channels
  
      self.last_layer = torch.nn.Conv1d(
          out_channels, final_out_channels, kernel_size=1
      )
  
      if zero_init:
          self.last_layer.weight.data *= 0
          self.last_layer.bias.data *= 0
  
imports: from typing import Optional, Tuple
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.cuda import amp
  from torch.cuda.amp import autocast
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import ConvNorm, LinearNorm, MaskedInstanceNorm1d
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths, sort_tensor, unsort_tensor
  from nemo.collections.tts.parts.utils.splines import piecewise_linear_inverse_transform, piecewise_linear_transform, unbounded_piecewise_quadratic_transform
label_desc: Use image node_id 1 to create a new SimpleConvNet object from inherited base classes: torch.nn with example: obj = SimpleConvNet(n_mel_channels, n_context_dim, final_out_channels, n_layers, kernel_size, with_dilation, max_channels, zero_init, use_partial_padding)