class_docstr: Causal (masked) convolution for [batch x time x depth] sequences.
  
  Maintains causality along time axis. Used in language modeling tasks.
code: def __init__(
      self,
      filters,
      kernel_width=3,
      kernel_initializer=None,
      bias_initializer=init.RandomNormalInitializer(1e-6),
      use_bias=True,
  ):
      super().__init__(
          filters=filters,
          kernel_size=(kernel_width,),
          strides=None,
          padding="VALID",
          dimension_numbers=("NWC", "WIO", "NWC"),
          kernel_initializer=kernel_initializer,
          bias_initializer=bias_initializer,
          use_bias=use_bias,
      )
  
imports: import functools
  import itertools
  import operator
  from trax import fastmath
  from trax.fastmath import numpy
  from trax.layers import base
  from trax.layers import initializers
label_desc: Use image node_id 1 to create a new CausalConv object from inherited base classes: Conv with example: obj = CausalConv(filters, kernel_width, kernel_initializer, bias_initializer, use_bias)