code: def conv1d(
      channels, kernel_size, in_channels, use_bias=True, **kwargs
  ):
      """
      Conv1D with better default initialization.
      """
      n = in_channels
      kernel_size = (
          kernel_size
          if isinstance(kernel_size, list)
          else [kernel_size]
      )
      for k in kernel_size:
          n *= k
      stdv = 1.0 / math.sqrt(n)
      winit = mx.initializer.Uniform(stdv)
      if use_bias:
          binit = mx.initializer.Uniform(stdv)
      else:
          binit = "zeros"
      return nn.Conv1D(
          channels=channels,
          kernel_size=kernel_size,
          in_channels=in_channels,
          use_bias=use_bias,
          weight_initializer=winit,
          bias_initializer=binit,
          **kwargs,
      )
  
imports: import math
  from typing import List, Optional
  import mxnet
  from mxnet import gluon
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
label_desc: Use image node_id 1 for calling a global function with example usage: conv1d(channels, kernel_size, in_channels, use_bias) and returns: nn