code: def index_reduce(
      input, dim, index, source, reduce, *, include_self=True, out=None
  ):
      result = ivy.copy_array(input)
      counts = (
          ivy.ones_like(result, dtype=result.dtype)
          if include_self
          else ivy.zeros_like(result, dtype=result.dtype)
      )
  
      index = index.astype(ivy.int64)
  
      def init_val(reduce):
          if reduce == "prod":
              return 1
          elif reduce == "amax":
              return -ivy.inf
          elif reduce == "amin":
              return ivy.inf
          else:
              return 0
  
      if not include_self:
          result[index, ...] = init_val(reduce)
  
      numel = index.size
      index_contig = ivy.copy_array(index)
  
      def update_counts(reduce, counts, dim, input_index):
          if reduce == "mean":
              counts_slice = [slice(None)] * counts.ndim
              counts_slice[dim] = input_index
              counts[tuple(counts_slice)] += 1
          return counts
  
      def update_result(result, reduce, input_data, source_data):
          if reduce == "prod":
              return input_data * source_data
          elif reduce == "amin":
              return ivy.minimum(input_data, source_data)
          elif reduce == "amax":
              return ivy.maximum(input_data, source_data)
          else:
              return input_data + source_data
  
      if result.ndim > 1:
          for i in range(numel):
              input_index = index_contig[i]
              if not (0 <= input_index < result.shape[dim]):
                  raise IndexError("Index out of range in self")
  
              input_data = ivy.gather(result, [input_index], axis=dim)
              source_data = ivy.gather(source, [i], axis=dim)
  
              result_slice = [slice(None)] * result.ndim
              result_slice[dim] = input_index
  
              update_data = update_result(
                  result, reduce, input_data, source_data
              )
              slide_shape = result[tuple(result_slice)].shape
              result[tuple(result_slice)] = ivy.reshape(
                  update_data, slide_shape
              )
  
              counts = update_counts(reduce, counts, dim, input_index)
  
      elif result.ndim == 1:
          for i in range(numel):
              input_index = index_contig[i]
              if not (0 <= input_index < result.size):
                  raise IndexError("Index out of range in self")
  
              input_data = ivy.flatten(result)[input_index]
              source_data = ivy.flatten(source)[i]
  
              result[input_index] = update_result(
                  result, reduce, input_data, source_data
              )
              counts[input_index] += 1
  
      if reduce == "mean":
          if ivy.any(counts == ivy.array(0)):
              counts[counts == ivy.array(0)] = ivy.array(1)
          result /= counts
  
          if not input.is_float_dtype():
              result = ivy.floor(result)
              result = result.astype(input.dtype)
  
      return result
  
imports: import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back, numpy_to_torch_style_args, to_ivy_shape
  import ivy.functional.frontends.torch
label_desc: Use image node_id 16 for calling a global function with example usage: index_reduce(input, dim, index, source, reduce) and returns: result, counts, int, unknown, unknown, ivy, ivy, int, ivy, unknown