class_docstr: 1D convolution with Gated mechanism, see the Wavenet papers described
  above.
  
  Parameters
  ----------
  inner_channels
      The dimensionality of the intermediate space
  
  out_channels
      The dimensionality of the output space
  
  kernel_size
      Specifies the dimensions of the convolution window.
  
  dilation
      Specifies the dilation rate to use for dilated convolution.
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docstr: Compute the 1D convolution with Gated mechanism.
  
  Parameters
  ----------
  x
      input features, shape (batch_size, num_features, sequence_length)
  
  Returns
  -------
  Tensor
      output, shape (batch_size, num_features, sequence_length)
docstr_tok: ["Compute","the","1D","convolution","with","Gated","mechanism",".","Parameters","--","--","--","--","--","x","input","features",",","shape","(","batch_size",",","num_features",",","sequence_length",")","Returns","--","--","--","-","Tensor","output",",","shape","(","batch_size",",","num_features",",","sequence_length",")"]
code: def hybrid_forward(self, F, x: Tensor) -> Tensor:
      """
      Compute the 1D convolution with Gated mechanism.
  
      Parameters
      ----------
      x
          input features, shape (batch_size, num_features, sequence_length)
  
      Returns
      -------
      Tensor
          output, shape (batch_size, num_features, sequence_length)
      """
      x1 = self.conv1(x)
      x2 = self.conv2(x)
      return self.output_conv(x1 * x2)
  
file_path: gluonts/src/gluonts/mx/block/cnn.py
imports: from typing import List, Optional, Tuple, Union
  from mxnet import gluon
  from mxnet.gluon import nn
  from gluonts.mx import Tensor
label_desc: Use image node_id 2 for calling the DilatedCausalGated obj's underlying member method code with example usage: obj.hybrid_forward(F, x) and returns: self