code: def _generate_data_layer_norm(
      draw,
      *,
      available_dtypes,
      large_abs_safety_factor=100,
      small_abs_safety_factor=100,
      safety_factor_scale="log",
      min_num_dims=1,
      max_num_dims=5,
      valid_axis=True,
      allow_neg_axes=False,
      max_axes_size=1,
      force_int_axis=True,
      ret_shape=True,
      abs_smallest_val=1000,
      allow_inf=False,
      allow_nan=False,
      exclude_min=True,
      exclude_max=True,
      min_value=-1000,
      max_value=1000,
      shared_dtype=False,
      min_dim_size=1,
      max_dim_size=3,
      group=False,
  ):
      results = draw(
          helpers.dtype_values_axis(
              available_dtypes=available_dtypes,
              large_abs_safety_factor=large_abs_safety_factor,
              small_abs_safety_factor=small_abs_safety_factor,
              safety_factor_scale=safety_factor_scale,
              min_num_dims=min_num_dims,
              max_num_dims=max_num_dims,
              min_dim_size=min_dim_size,
              max_dim_size=max_dim_size,
              valid_axis=valid_axis,
              allow_neg_axes=allow_neg_axes,
              max_axes_size=max_axes_size,
              force_int_axis=force_int_axis,
              ret_shape=ret_shape,
          )
      )
  
      dtype, values, axis, shape = results
  
      if group:
          channel_size = shape[1]
          group_list = [*range(1, max_dim_size)]
          group_list = list(
              filter(lambda x: (channel_size % x == 0), group_list)
          )
          group_size = draw(st.sampled_from(group_list))
          weight_shape = [shape[1]]
          bias_shape = [shape[1]]
      else:
          weight_shape = shape[axis:]
          bias_shape = shape[axis:]
  
      arg_dict = {
          "available_dtypes": dtype,
          "abs_smallest_val": abs_smallest_val,
          "min_value": min_value,
          "max_value": max_value,
          "large_abs_safety_factor": large_abs_safety_factor,
          "small_abs_safety_factor": small_abs_safety_factor,
          "allow_inf": allow_inf,
          "allow_nan": allow_nan,
          "exclude_min": exclude_min,
          "exclude_max": exclude_max,
          "min_num_dims": min_num_dims,
          "max_num_dims": max_num_dims,
          "shared_dtype": shared_dtype,
          "ret_shape": False,
      }
  
      results_weight = draw(
          helpers.dtype_and_values(shape=weight_shape, **arg_dict)
      )
      results_bias = draw(
          helpers.dtype_and_values(shape=bias_shape, **arg_dict)
      )
      results_new_std = draw(
          helpers.dtype_and_values(shape=shape, **arg_dict)
      )
  
      _, weight_values = results_weight
      _, bias_values = results_bias
      _, new_std_values = results_new_std
  
      axis = shape[axis:]
      if group:
          return dtype, values, weight_values, bias_values, group_size
      return (
          dtype,
          values,
          axis,
          weight_values,
          bias_values,
          new_std_values,
      )
  
file_path: ivy/ivy_tests/test_ivy/test_frontends/test_torch/test_nn/test_functional/test_norms.py
imports: from hypothesis import strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 1 for calling a global function with example usage: _generate_data_layer_norm(draw) and returns: dtype,  values,  axis,  weight_values,  bias_values,  new_std_values, dtype,  values,  weight_values,  bias_values,  group_size