class_docstr: Implement CrossentropySoftmaxArgmax1HotWithBias on the gpu.
code: def c_code(self, node, nodename, inp, out, sub):
      itemsize_x = np.dtype(node.inputs[0].dtype).itemsize
      worksize_x = np.dtype(work_dtype(node.inputs[0].dtype)).itemsize
      itemsize_b = np.dtype(node.inputs[1].dtype).itemsize
      itemsize_y_idx = np.dtype(node.inputs[2].dtype).itemsize
      itemsize_nll = np.dtype(node.outputs[0].dtype).itemsize
      itemsize_sm = np.dtype(node.outputs[1].dtype).itemsize
      itemsize_am = np.dtype(node.outputs[2].dtype).itemsize
      x, b, y_idx = inp
      nll, sm, am = out
      fail = sub["fail"]
      ctx = sub["params"]
      k_var = "k_xent_sm_1hot_bias_%(nodename)s" % locals()
      err_check = (
          """
              if (err != GA_NO_ERROR) {
                  PyErr_Format(PyExc_RuntimeError,
                               "gpuarray error: %(k_var)s: %%s.",
                               GpuKernel_error(&%(k_var)s, err));
                  %(fail)s;
              }
          """
          % locals()
      )
      sio = StringIO()
      print(
          """
          if (PyGpuArray_DIMS(%(x)s)[0] !=
              PyGpuArray_DIMS(%(y_idx)s)[0])
          {
              PyErr_SetString(PyExc_ValueError,
                              "dimension mismatch in x,y_idx arguments");
              %(fail)s;
          }
          if (PyGpuArray_DIMS(%(x)s)[1] != PyGpuArray_DIMS(%(b)s)[0])
          {
              PyErr_SetString(PyExc_ValueError,
                              "dimension mismatch in x,b arguments");
              %(fail)s;
          }
          if (theano_prep_output(&%(nll)s, 1, PyGpuArray_DIMS(%(y_idx)s), %(x)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
          if (theano_prep_output(&%(sm)s, 2, PyGpuArray_DIMS(%(x)s), %(x)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
          if (theano_prep_output(&%(am)s, 1, PyGpuArray_DIMS(%(y_idx)s), %(y_idx)s->ga.typecode, GA_C_ORDER, %(ctx)s)) %(fail)s
          {
              size_t n_blocks = std::min(PyGpuArray_DIM(%(x)s, 0), (size_t)4096);
              size_t n_threads = std::min(PyGpuArray_DIM(%(x)s, 1), (size_t)256);
              size_t n_shared = n_threads * %(worksize_x)s;
       //TODO: launch more threads per row and do parallel sum and max reductions
              int err = k_xent_sm_1hot_bias_call(
                  1, &n_blocks, &n_threads, n_shared,
                  PyGpuArray_DIMS(%(x)s)[0],
                  PyGpuArray_DIMS(%(x)s)[1],
                  %(x)s->ga.data, %(x)s->ga.offset,
                  PyGpuArray_STRIDE(%(x)s, 0) / %(itemsize_x)s,
                  PyGpuArray_STRIDE(%(x)s, 1) / %(itemsize_x)s,
                  %(b)s->ga.data, %(b)s->ga.offset,
                  PyGpuArray_STRIDE(%(b)s, 0) / %(itemsize_b)s,
                  %(y_idx)s->ga.data, %(y_idx)s->ga.offset,
                  PyGpuArray_STRIDE(%(y_idx)s, 0) / %(itemsize_y_idx)s,
                  %(nll)s->ga.data, %(nll)s->ga.offset,
                  PyGpuArray_STRIDE(%(nll)s, 0) / %(itemsize_nll)s,
                  %(sm)s->ga.data, %(sm)s->ga.offset,
                  PyGpuArray_STRIDE(%(sm)s, 0) / %(itemsize_sm)s,
                  PyGpuArray_STRIDE(%(sm)s, 1) / %(itemsize_sm)s,
                  %(am)s->ga.data, %(am)s->ga.offset,
                  PyGpuArray_STRIDE(%(am)s, 0) / %(itemsize_am)s);
              %(err_check)s
          }
          """
          % locals(),
          file=sio,
      )
      return sio.getvalue()
  
imports: from __future__ import absolute_import, print_function, division
  import numpy
  from theano import Op, Apply
  from six import StringIO
  from .basic_ops import as_gpuarray_variable, GpuKernelBase, Kernel, gpuarray_helper_inc_dir, infer_context_name
  from .type import GpuArrayType
  from .fp16_help import work_dtype, load_w, write_w
label_desc: Use image node_id 5 for calling the GpuCrossentropySoftmaxArgmax1HotWithBias obj's underlying member method code with example usage: obj.c_code(node, nodename, inp, out, sub) and returns: sio