class_docstr: Validates potential Mesh TensorFlow layouts.
  
  Usage Example:
    mtf_graph = mtf.Graph()
    # Add operations to mtf_graph using Mesh TensorFlow.
    mesh_shape = mtf.Shape([("m1", 4), ("m2", 2)])
    layout_validator = valid_layouts.LayoutValidator(mtf_graph, mesh_shape)
  
    print(layout_validator.splittable_mtf_dimension_names)
    # Set of names of Mesh TensorFlow dimensions that may be assigned to mesh
    # dimensions.
  
    print(layout_validator.is_valid_assignment("batch", "m1"))
    # Whether the 'batch' Mesh TensorFlow dimension may be assigned to the 'm1'
    # mesh dimension. Unlike the previous method, this ensures that every
    # occurrence of the 'batch' dimension has a size that is evenly divisible by
    # the size of 'm1'.
  
  Attributes:
    splittable_mtf_dimension_names: a set(string) of the names of MTF dimensions
        that may be assigned in a layout.
    mesh_dimension_name_to_size: a {string: int}, mapping names of mesh
        dimensions to their size.
code: def mesh_dimension_name_to_size(self):
      return self._mesh_dimension_name_to_size
  
file_path: mesh/mesh_tensorflow/auto_mtf/valid_layouts.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import re
label_desc: Use image node_id 3 for calling the LayoutValidator obj's underlying member method code with example usage: obj.mesh_dimension_name_to_size() and returns: self