message: Clarify dimension variable scoping
diff: @@ -349,6 +349,8 @@ The name of each dimension variable MUST adhere to C identifier syntax.
  Currently, dimension variables are not scoped. A dimension variable "N" represents the same value across the entire graph in a model. For example, if the graph has two inputs X and Y each with shape ["N"], then at runtime the values passed in for X and Y MUST be tensors of rank 1 with the same dimension. Nested sub-graphs currently share the same scope for dimension variables as the main-graph. This allows a model to relate the dimensions of tensors inside the subgraph to the dimensions of tensors in the outer graph.
  +ONNXML supports richer types such as Sequences of Tensors. The global scoping of dimension variables means that a variable with type "Sequence<Tensor<float, [M,N]>" represents a sequence of tensors that *all have the same shape*. The dimension variables M or N must be omitted from the above type if that dimension does not have a fixed size across all tensors in the sequence. The entire shape must be omitted from the type if different tensors in the sequence may have different ranks.
  +
  For example, a graph that performs matrix cross-product may be defined as taking two inputs of shape [K,M] and [M,N], and producing an output of shape [K,N].
  Shapes MAY be defined using a combination of integers and variables.
  @@ -356,7 +358,8 @@ Shapes MAY be defined using a combination of integers and variables.
  _Historical Notes_: The following extensions were considered early on, but were never implemented or supported.
  * The use of an empty string (as a dimension variable) to denote an unknown dimension not related to any other dimension. This was discarded in favor of using a Dimension with neither dim_value nor dim_param set.
  * The use of the string "\*" (as a dimension variable) to denote a sequence of zero or more dimensions of unknown cardinality. This is not supported. In the current implementation, the number of dimensions in a shape MUST represent the rank of the tensor. A tensor of unknown rank is represented using a TypeProto::Tensor object with no shape, which is legal.
  -* ONNXML supports richer types such as Sequences of Tensors. A scoping mechanism for the dimension variables may be useful to distinguish between the following two types: a sequence of square matrices (of differing sizes) vs a sequence of square matrices (all of same size). This is not currently supported.
  +* A scoping mechanism to allow dimension variables that are local to a sub-graph (such as the body of a loop) may be useful, but is not currently supported.
  +* ONNXML supports richer types such as Sequences of Tensors. A scoping mechanism for the dimension variables local to a type may be useful to distinguish between the following two types: a sequence of square matrices (of differing sizes) vs a sequence of square matrices (all of same size). This is not currently supported.
  ### Attribute Types
  