code: def _causalmax(x, dim, pool_len, padding_type="left"):
      kernel_size = [1, 1, 1]
      pool_len = pool_len
      kernel_size[dim] = pool_len
      padding = [[0, 0], [0, 0], [0, 0]]
      if padding_type == "left":
          padding[dim] = [pool_len - 1, 0]
      elif padding_type == "right":
          padding[dim] = [0, pool_len - 1]
      elif padding_type == "even":
          leng = pool_len - 1
          padding[dim] = [leng // 2, leng - leng // 2]
      else:
          raise ValueError(
              "Padding type {} not supported".format(padding_type)
          )
      padded_x = tf.pad(x, padding, "CONSTANT", constant_values=-1e4)
  
      kernel_size.insert(2, 1)
  
      return tf.squeeze(
          tf.nn.max_pool(
              input=tf.expand_dims(padded_x, 2),
              ksize=kernel_size,
              strides=[1, 1, 1, 1],
              padding="VALID",
          ),
          axis=2,
      )
  
file_path: finetune/finetune/custom_ops/indico_tf_ops/python/ra.py
imports: import os
  import math
  import tensorflow
  from finetune.custom_ops.indico_tf_ops.python import indico_ops
label_desc: Use image node_id 1 for calling a global function with example usage: _causalmax(x, dim, pool_len, padding_type) and returns: tf