code: def pad_and_partition(
      tensor: tf.Tensor, segment_len: int
  ) -> tf.Tensor:
      """
      Pad and partition a tensor into segment of len `segment_len`
      along the first dimension. The tensor is padded with 0 in order
      to ensure that the first dimension is a multiple of `segment_len`.
  
      Examples:
      ```python
      >>> tensor = [[1, 2, 3], [4, 5, 6]]
      >>> segment_len = 2
      >>> pad_and_partition(tensor, segment_len)
      [[[1, 2], [4, 5]], [[3, 0], [6, 0]]]
      ````
  
      Parameters:
          tensor (tf.Tensor):
              Tensor of known fixed rank
          segment_len (int):
              Segment length.
  
      Returns:
          tf.Tensor:
              Padded and partitioned tensor.
      """
      tensor_size = tf.math.floormod(tf.shape(tensor)[0], segment_len)
      pad_size = tf.math.floormod(
          segment_len - tensor_size, segment_len
      )
      padded = tf.pad(
          tensor, [[0, pad_size]] + [[0, 0]] * (len(tensor.shape) - 1)
      )
      split = (tf.shape(padded)[0] + segment_len - 1) // segment_len
      return tf.reshape(
          padded,
          tf.concat(
              [[split, segment_len], tf.shape(padded)[1:]], axis=0
          ),
      )
  
file_path: spleeter/spleeter/utils/tensor.py
imports: from typing import Any, Callable, Dict
  import pandas
  import tensorflow
label_desc: Use image node_id 4 for calling a global function with example usage: pad_and_partition(tensor, segment_len) and returns: tf