class_docstr: Conv1d with some builtin handling of asymmetric or causal padding
  and normalization.
code: def forward(self, x):
      B, C, T = x.shape
      kernel_size = self.conv.conv.kernel_size[0]
      stride = self.conv.conv.stride[0]
      dilation = self.conv.conv.dilation[0]
      kernel_size = (
          kernel_size - 1
      ) * dilation + 1  # effective kernel size with dilations
      padding_total = kernel_size - stride
      extra_padding = get_extra_padding_for_conv1d(
          x, kernel_size, stride, padding_total
      )
      if self.causal:
          # Left padding for causal
          x = pad1d(
              x, (padding_total, extra_padding), mode=self.pad_mode
          )
      else:
          # Asymmetric padding required for odd strides
          padding_right = padding_total // 2
          padding_left = padding_total - padding_right
          x = pad1d(
              x,
              (padding_left, padding_right + extra_padding),
              mode=self.pad_mode,
          )
      return self.conv(x)
  
imports: import math
  import typing
  import warnings
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.utils import spectral_norm, weight_norm
label_desc: Use image node_id 2 for calling the StreamableConv1d obj's underlying member method code with example usage: obj.forward(x) and returns: self