TrainingExtensions/torch/src/python/aimet_torch/meta/connectedgraph.py
def _fill_op_params(self):
        """
        For certain ops like convolution, batch norm, and linear, create products for their parameters if they don't
        exist yet.
        """
        for op in self._ops.values():
            module = op.get_module()
            if module is not None:
                name = self._module_to_name.get(module, None)
                if op.type in ['Conv', 'ConvTranspose', 'BatchNormalization', 'Gemm']:
                    if module.weight is not None:
                        product_name = name + '.weight'
                        self._create_and_add_param_product_if_not_exists(op, product_name, list(module.weight.shape))
                    if module.bias is not None:
                        product_name = name + '.bias'
                        self._create_and_add_param_product_if_not_exists(op, product_name, list(module.bias.shape))
                if op.type == 'BatchNormalization':
                    # If batch_norm, fill in rest of bn params
                    if module.running_mean is not None:
                        product_name = name + '.running_mean'
                        self._create_and_add_param_product_if_not_exists(op, product_name,
                                                                         list(module.running_mean.shape))
                    if module.running_var is not None:
                        product_name = name + '.running_var'
                        self._create_and_add_param_product_if_not_exists(op, product_name,
                                                                         list(module.running_var.shape))

        For certain ops like convolution, batch norm, and linear, create products for their parameters if they don't
        exist yet.
        
For certain ops like convolution, batch norm, and linear, create products for their parameters if they don't
exist yet.
For certain ops like convolution batch norm and linear create products for their parameters if they don ' t exist yet .
def _fill_op_params(self):
        for op in self._ops.values():
            module = op.get_module()
            if module is not None:
                name = self._module_to_name.get(module, None)
                if op.type in ['Conv', 'ConvTranspose', 'BatchNormalization', 'Gemm']:
                    if module.weight is not None:
                        product_name = name + '.weight'
                        self._create_and_add_param_product_if_not_exists(op, product_name, list(module.weight.shape))
                    if module.bias is not None:
                        product_name = name + '.bias'
                        self._create_and_add_param_product_if_not_exists(op, product_name, list(module.bias.shape))
                if op.type == 'BatchNormalization':
                    if module.running_mean is not None:
                        product_name = name + '.running_mean'
                        self._create_and_add_param_product_if_not_exists(op, product_name,
                                                                         list(module.running_mean.shape))
                    if module.running_var is not None:
                        product_name = name + '.running_var'
                        self._create_and_add_param_product_if_not_exists(op, product_name,
                                                                         list(module.running_var.shape))
def _fill_op_params ( self ) : for op in self . _ops . values ( ) : module = op . get_module ( ) if module is not None : name = self . _module_to_name . get ( module , None ) if op . type in [ 'Conv' , 'ConvTranspose' , 'BatchNormalization' , 'Gemm' ] : if module . weight is not None : product_name = name + '.weight' self . _create_and_add_param_product_if_not_exists ( op , product_name , list ( module . weight . shape ) ) if module . bias is not None : product_name = name + '.bias' self . _create_and_add_param_product_if_not_exists ( op , product_name , list ( module . bias . shape ) ) if op . type == 'BatchNormalization' : if module . running_mean is not None : product_name = name + '.running_mean' self . _create_and_add_param_product_if_not_exists ( op , product_name , list ( module . running_mean . shape ) ) if module . running_var is not None : product_name = name + '.running_var' self . _create_and_add_param_product_if_not_exists ( op , product_name , list ( module . running_var . shape ) )
For certain ops like convolution, batch norm, and linear, create products for their parameters if they don't
exist yet.
For certain ops like convolution batch norm and linear create products for their parameters if they don ' t exist yet .
"""
        For certain ops like convolution, batch norm, and linear, create products for their parameters if they don't
        exist yet.
        """ # If batch_norm, fill in rest of bn params
self