class_docstr: Distributed optimizer, for all data types (fp16, bf16, and fp32).
  
  Arguments:
      optimizer: base optimizer such as Adam or SGD
      clip_grad: clip gradeints with this global L2 norm. Note
          that clipping is ignored if clip_grad == 0
      log_num_zeros_in_grad: return number of zeros in the gradients.
      check_for_nan_in_grad: check if gradients have a NaN.
      params_have_main_grad: flag indicating if parameters have
          a `main_grad` field. If this is set, we are assuming
          that the model parameters are store in the `main_grad`
          field instead of the typical `grad` field. This happens
          for the DDP cases where there is a continuous buffer
          holding the gradients. For example for bfloat16, we want
          to do gradient accumulation and all-reduces in float32
          and as a result we store those gradients in the main_grad.
          Note that main grad is not necessarily in float32.
      fp16: if true, the model is running in fp16.
      bf16: if true, the model is running in bfloat16.
      grad_scaler: used for scaling gradients. Note that this can be
          None. This case happens when `bf16 = True` and we don't
          use any loss scale. Note that for `bf16 = True`, we can have
          a constnat gradient scaler. Also for `bf16 = False`, we
          always require a grad scaler.
      models: list of models (i.e., the virtual pipelining models). This
          is used by the distributed optimizer for mapping parameters.
docstr: Create param-to-grad-buffer mappings, for grad buffer data types
  within a specific virtual model.
docstr_tok: ["Create","param-to-grad-buffer","mappings",",","for","grad","buffer","data","types","within","a","specific","virtual","model","."]
code: def build_model_gbuf_range_map(cls, model):
      """
      Create param-to-grad-buffer mappings, for grad buffer data types
      within a specific virtual model.
      """
      # Iterate through all buckets to construct param ranges that this rank "owns"
      # (the dp_rank'th shard of each bucket, where each shard is 1/dp_world_size
      # of the bucket).
      return {
          dtype: [
              cls.build_model_gbuf_range(model, dtype, bucket_index)
              for bucket_index in range(
                  len(model.grad_buffers[dtype].buckets)
              )
          ]
          for dtype in model.grad_buffers
      }
  
imports: from apex.optimizers import FusedAdam
  import math
  import torch
  from megatron import get_args
  from megatron import get_timers
  from megatron import print_rank_0
  from megatron.core import mpu, tensor_parallel
  from .optimizer import MixedPrecisionOptimizer, _zero_grad_group_helper
  from .utils import shard_buffer
label_desc: Use image node_id 3 for calling the DistributedOptimizer obj's underlying member method code with example usage: obj.build_model_gbuf_range_map(cls, model) and returns: unknown