diff --git a/egs/librispeech/ASR/transducer_stateless/diagnostics.py b/egs/librispeech/ASR/transducer_stateless/diagnostics.py
index 1a2324775..088ef14cb 100644
--- a/egs/librispeech/ASR/transducer_stateless/diagnostics.py
+++ b/egs/librispeech/ASR/transducer_stateless/diagnostics.py
@@ -31,32 +31,33 @@ class TensorDiagnosticOptions(object):
 
 
 
-def get_sum_abs_stats(x: Tensor, dim: int,
+def get_tensor_stats(x: Tensor, dim: int,
                       stats_type: str) -> Tuple[Tensor, int]:
     """
-    Returns the sum-of-absolute-value of this Tensor, for each
-    index into the specified axis/dim of the tensor.
+    Returns the specified transformation of the Tensor (either x or x.abs()
+    or (x > 0), summed over all but the index `dim`.
+
     Args:
        x: Tensor, tensor to be analyzed
       dim: dimension with 0 <= dim < x.ndim
-      stats_type: either "mean-abs" in which case the stats represent the
-           mean absolute value, or "pos-ratio" in which case the
-           stats represent the proportion of positive values (actually:
-           the tensor is count of positive values, count is the count of
-           all values).
-    Returns (sum_abs, count)
-       where sum_abs is a Tensor of shape (x.shape[dim],), and the count
+      stats_type:
+          "mean-abs" or "abs-value" -> take abs() before summing
+          "pos-ratio" -> take (x > 0) before summing
+          "value -> just sum x itself
+    Returns (stats, count)
+       where stats is a Tensor of shape (x.shape[dim],), and the count
        is an integer saying how many items were counted in each element
-       of sum_abs.
+       of stats.
     """
-    if stats_type == "mean-abs":
+    if stats_type == "mean-abs" or stats_type == "abs-value":
         x = x.abs()
-    else:
-        assert stats_type == "pos-ratio"
+    elif stats_type == "pos-ratio":
         x = (x > 0).to(dtype=torch.float)
+    else:
+        assert stats_type == "value"
     orig_numel = x.numel()
     sum_dims = [ d for d in range(x.ndim) if d != dim ]
-    if len(sum_dims) != 0:
+    if len(sum_dims) > 0:
         x = torch.sum(x, dim=sum_dims)
     count = orig_numel // x.numel()
     x = x.flatten()
@@ -80,7 +81,7 @@ def get_diagnostics_for_dim(dim: int, tensors: List[Tensor],
      see the code.
     """
     # stats_and_counts is a list of pair (Tensor, int)
-    stats_and_counts = [ get_sum_abs_stats(x, dim, stats_type) for x in tensors ]
+    stats_and_counts = [ get_tensor_stats(x, dim, stats_type) for x in tensors ]
     stats = [ x[0] for x in stats_and_counts ]
     counts = [ x[1] for x in stats_and_counts ]
     if sizes_same:
@@ -115,9 +116,12 @@ def get_diagnostics_for_dim(dim: int, tensors: List[Tensor],
 
 def print_diagnostics_for_dim(name: str, dim: int, tensors: List[Tensor],
                               options: TensorDiagnosticOptions):
+    ndim = tensors[0].ndim
+    # options.stats_types() should return [ "mean-abs", "pos-ratio" ] in the
+    # normal case.
+    stats_types = options.stats_types() if ndim > 1 else [ "value", "abs-value" ]
 
-    for stats_type in options.stats_types():
-        # stats_type will be "mean-abs" or "pos-ratio".
+    for stats_type in stats_types:
         sizes = [ x.shape[dim] for x in tensors ]
         sizes_same = all([ x == sizes[0] for x in sizes ])
         s = get_diagnostics_for_dim(dim, tensors,