code: def log_combinations(n, counts, name="log_combinations"):
      """Multinomial coefficient.
  
      Given `n` and `counts`, where `counts` has last dimension `k`, we compute
      the multinomial coefficient as:
  
      ```n! / sum_i n_i!```
  
      where `i` runs over all `k` classes.
  
      Args:
        n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
          outcomes.
        counts: Floating-point `Tensor` broadcastable with `n`. This represents
          counts in `k` classes, where `k` is the last dimension of the tensor.
        name: A name for this operation (optional).
  
      Returns:
        `Tensor` representing the multinomial coefficient between `n` and `counts`.
      """
      # First a bit about the number of ways counts could have come in:
      # E.g. if counts = [1, 2], then this is 3 choose 2.
      # In general, this is (sum counts)! / sum(counts!)
      # The sum should be along the last dimension of counts. This is the
      # "distribution" dimension. Here n a priori represents the sum of counts.
      with ops.name_scope(name, values=[n, counts]):
          n = ops.convert_to_tensor(n, name="n")
          counts = ops.convert_to_tensor(counts, name="counts")
          total_permutations = math_ops.lgamma(n + 1)
          counts_factorial = math_ops.lgamma(counts + 1)
          redundant_permutations = math_ops.reduce_sum(
              counts_factorial, axis=[-1]
          )
          return total_permutations - redundant_permutations
  
imports: import functools
  import hashlib
  import numpy
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import check_ops
  from tensorflow.python.ops import cond
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.util import tf_inspect
label_desc: Use image node_id 15 for calling a global function with example usage: log_combinations(n, counts, name) and returns: unknown