class_docstr: Causal (masked) convolution for [batch x time x depth] sequences.
  
  Maintains causality along time axis. Used in language modeling tasks.
code: def forward(self, x):
      assert self._padding == "VALID"
      # Left pad with 0s. Applying an unmasked valid convolution on top of this
      # yields a causal convolution.
      # TODO(ddohan): Support strided and dilated convolutions.
      rate = 1
      effective_kernel_size = int((self._kernel_size[0] - 1) * rate + 1)
      pad = effective_kernel_size - 1
      x_leftpad = jnp.pad(
          x, pad_width=[[0, 0], [pad, 0], [0, 0]], mode="constant"
      )
      return super().forward(x_leftpad)
  
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 2 for calling the CausalConv obj's underlying member method code with example usage: obj.forward(x) and returns: super