class_docstr: A 1D convolutional transposed layer for PyTorch.
  This layer applies a 1D convolutional transpose operation to its input tensor,
  where the number of channels of the input tensor is the same as the number of channels of the output tensor.
  
  Attributes:
      in_channels (int): The number of channels in the input tensor.
      out_channels (int): The number of channels in the output tensor.
      kernel_size (int): The size of the convolutional kernel. Default: 1.
      padding (int): The number of padding elements to add to the input tensor. Default: 0.
      conv (BSConv1d): The 1D convolutional transpose layer.
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code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int = 1,
      padding: int = 0,
  ):
      super().__init__()
      self.conv = BSConv1d(
          in_channels,
          out_channels,
          kernel_size=kernel_size,
          padding=padding,
      )
  
imports: from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils import parametrize
  from TTS.tts.layers.delightful_tts.kernel_predictor import KernelPredictor
label_desc: Use image node_id 1 to create a new ConvTransposed object from inherited base classes: nn with example: obj = ConvTransposed(in_channels, out_channels, kernel_size, padding)