class_docstr: Model helper for building models on top of layers abstractions.
  
  Each layer is the abstraction that is higher level than Operator. Layer
  is responsible for ownership of it's own parameters and can easily be
  instantiated in multiple nets possible with different sets of ops.
  As an example: one can easily instantiate predict and train nets from
  the same set of layers, where predict net will have subset of the
  operators from train net.
class_docstr_tok: ["Model","helper","for","building","models","on","top","of","layers","abstractions",".","Each","layer","is","the","abstraction","that","is","higher","level","than","Operator",".","Layer","is","responsible","for","ownership","of","it","'s","own","parameters","and","can","easily","be","instantiated","in","multiple","nets","possible","with","different","sets","of","ops",".","As","an","example",":","one","can","easily","instantiate","predict","and","train","nets","from","the","same","set","of","layers",",","where","predict","net","will","have","subset","of","the","operators","from","train","net","."]
code: def input_feature_schema(self):
      return self._input_feature_schema
  
imports: from caffe2.python import core, model_helper, schema, scope, utils, muji
  from caffe2.python.modeling.parameter_info import ParameterInfo
  from caffe2.python.modeling.parameter_sharing import parameter_sharing_context
  from caffe2.python.modeling.net_modifier import NetModifier
  from caffe2.python.optimizer import get_param_device, Optimizer
  from caffe2.python.regularizer import Regularizer, RegularizationBy
  from caffe2.python.layers import layers
  import logging
  import numpy
  import copy
label_desc: Use image node_id 27 for calling the LayerModelHelper obj's underlying member method code with example usage: obj.input_feature_schema() and returns: self