class_docstr: This class defines core TCN architecture.
  This is only the base class, training strategy is not implemented.
docstr: Defines the temporal block, which is a dilated causual conv layer,
  followed by relu and dropout
  Args:
      x: Input to temporal block
      out_channels: Number of conv filters
      dilation: dilation rate
      padding: padding value
  
  Returns:
      Tensor output of temporal block
docstr_tok: ["Defines","the","temporal","block",",","which","is","a","dilated","causual","conv","layer",",","followed","by","relu","and","dropout","Args",":","x",":","Input","to","temporal","block","out_channels",":","Number","of","conv","filters","dilation",":","dilation","rate","padding",":","padding","value","Returns",":","Tensor","output","of","temporal","block"]
code: def _temporal_block(self, x, out_channels, dilation, padding):
      """
      Defines the temporal block, which is a dilated causual conv layer,
      followed by relu and dropout
      Args:
          x: Input to temporal block
          out_channels: Number of conv filters
          dilation: dilation rate
          padding: padding value
  
      Returns:
          Tensor output of temporal block
      """
      # conv layer
      x = self._dilated_causal_conv(x, out_channels, dilation, padding)
  
      x = tf.nn.relu(x)
  
      # dropout
      batch_size = tf.shape(x)[0]
      x = tf.layers.dropout(
          x,
          rate=self.dropout,
          noise_shape=[batch_size, 1, out_channels],
          training=self.training_mode,
      )
  
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras.layers import Wrapper
  from tensorflow.python.layers.convolutional import Conv1D
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.eager import context
  from tensorflow.python.ops import nn_impl
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras.engine.base_layer import InputSpec
  from tensorflow.python.ops import array_ops
  from tensorflow.python.framework import ops
label_desc: Use image node_id 5 for calling the TCN obj's underlying member method code with example usage: obj._temporal_block(x, out_channels, dilation, padding) and returns: x