code: def inline_reduce_fixed_shared(
      N,
      buf,
      x,
      stride_x,
      load_x,
      pos,
      count,
      manner_fn,
      manner_init,
      b="",
      stride_b="",
      load_b="",
      dtype="float32",
  ):
      """
      Return C++ code for a function that reduces a contiguous buffer.
  
      This function leaves the answer in position 0 of the buffer. The
      rest of the buffer is trashed by this function.
  
      Parameters
      ----------
      N
          Length of the buffer.
      buf
          Buffer pointer of size warpSize * sizeof(dtype).
      x
          Input data.
      stride_x
          Input data stride.
      load_x
          Wrapper to read from x.
      pos
          Index of executing thread.
      count
          Number of executing threads.
      manner_fn
          A function that accepts strings of arguments a and b, and
          returns c code for their reduction.
  
            return "%(a)s + %(b)s"
  
          for a sum reduction.
      manner_init
          A function that accepts strings of arguments a and return c
          code for its initialization.
      b
          Optional, pointer to the bias.
      stride_b
          Optional, the stride of b if b is provided.
      load_b
          Optional, wrapper to read from b if b is provided.
      dtype
          Optional, the dtype of the output.
  
      Notes
      -----
      `buf` should be in gpu shared memory, we access it many times.
  
      """
      if b:
          init = manner_init(
              "%(load_x)s(%(x)s[%(pos)s * %(stride_x)s]) +"
              " %(load_b)s(%(b)s[%(pos)s * %(stride_b)s])" % locals()
          )
          loop_line = manner_fn(
              "red",
              manner_init(
                  "%(load_x)s(%(x)s[i * %(stride_x)s]) + "
                  "%(load_b)s(%(b)s[i * %(stride_b)s])" % locals()
              ),
          )
      else:
          init = manner_init(
              "%(load_x)s(%(x)s[%(pos)s * %(stride_x)s])" % locals()
          )
          loop_line = manner_fn(
              "red",
              manner_init(
                  "%(load_x)s(%(x)s[i * %(stride_x)s])" % locals()
              ),
          )
      loop_line2 = manner_fn("%s[%s]" % (buf, pos), "%s[i]" % buf)
      r_n = manner_fn("%s[%s]" % (buf, pos), "%s[%s+_n]" % (buf, pos))
  
      ctype = gpuarray.dtype_to_ctype(dtype)
      return (
          """
      {
          // This function trashes buf[1..n_threads],
          // leaving the reduction result in buf[0].
          %(ctype)s red = %(init)s;
          #pragma unroll 16
          for (int i = %(pos)s + %(count)s; i<%(N)s; i += %(count)s) {
            red = %(loop_line)s;
          }
          buf[%(pos)s] = red;
          __syncthreads();
          if (%(pos)s < warpSize) {
              for (int i = %(pos)s + warpSize; i < %(count)s; i += warpSize) {
                  %(buf)s[%(pos)s] = %(loop_line2)s;
              }
          }
          __syncthreads();
          //reduce so that %(pos)s 0 has the reduction of everything
          for (unsigned int _n = warpSize / 2; _n > 0; _n /= 2) {
            if (%(pos)s < _n && %(pos)s + _n < %(N)s)
              %(buf)s[%(pos)s] = %(r_n)s;
            __syncthreads();
          }
      }
      """
          % locals()
      )
  
file_path: Theano/theano/gpuarray/kernel_codegen.py
imports: from __future__ import absolute_import, print_function, division
label_desc: Use image node_id 9 for calling a global function with example usage: inline_reduce_fixed_shared(N, buf, x, stride_x, load_x, pos, count, manner_fn, manner_init, b, stride_b, load_b, dtype) and returns: unknown