original_docstring: Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
      and return the created node.
      
docstring: Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
  and return the created node.
docstring_tokens: Map MXNet ' s FullyConnected operator attributes to onnx ' s Gemm operator and return the created node .
code: def convert_fully_connected(node, **kwargs):
      """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
      and return the created node.
      """
      onnx = import_onnx_modules()
      name = node["name"]
      inputs = node["inputs"]
      attrs = node["attrs"]
      initializer = kwargs["initializer"]
  
      no_bias = get_boolean_attribute_value(attrs, "no_bias")
  
      input_node_id = kwargs["index_lookup"][inputs[0][0]]
      weight_node_id = kwargs["index_lookup"][inputs[1][0]]
  
      proc_nodes = kwargs["proc_nodes"]
  
      input_node = proc_nodes[input_node_id]
      input_name = input_node.name
  
      weights_node = proc_nodes[weight_node_id]
      weights_name = weights_node.name
  
      fcnode = []
  
      if no_bias == 0:
          bias_node_id = kwargs["index_lookup"][inputs[2][0]]
          bias_node = proc_nodes[bias_node_id]
          bias_name = bias_node.name
      else:
          data_type = onnx.mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype('int64')]
          bias_name = "bias" + str(kwargs["idx"])
          tensor_node = onnx.helper.make_tensor_value_info(bias_name, data_type, (1,))
          initializer.append(
              onnx.helper.make_tensor(
                  name=bias_name,
                  data_type=data_type,
                  dims=(1,),
                  vals=[0],
                  raw=False,
              )
          )
          fcnode.append(tensor_node)
  
      node = onnx.helper.make_node(
          "Gemm",
          [input_name, weights_name, bias_name],  # input (A, B, C) - C can be in place
          [name],  # output
          alpha=1.0,
          beta=1.0,
          transA=False,
          transB=True,
          name=name
      )
  
      fcnode.append(node)
  
      return fcnode
code_tokens: def convert_fully_connected ( node , ** kwargs ) : onnx = import_onnx_modules ( ) name = node [ "name" ] inputs = node [ "inputs" ] attrs = node [ "attrs" ] initializer = kwargs [ "initializer" ] no_bias = get_boolean_attribute_value ( attrs , "no_bias" ) input_node_id = kwargs [ "index_lookup" ] [ inputs [ 0 ] [ 0 ] ] weight_node_id = kwargs [ "index_lookup" ] [ inputs [ 1 ] [ 0 ] ] proc_nodes = kwargs [ "proc_nodes" ] input_node = proc_nodes [ input_node_id ] input_name = input_node . name weights_node = proc_nodes [ weight_node_id ] weights_name = weights_node . name fcnode = [ ] if no_bias == 0 : bias_node_id = kwargs [ "index_lookup" ] [ inputs [ 2 ] [ 0 ] ] bias_node = proc_nodes [ bias_node_id ] bias_name = bias_node . name else : data_type = onnx . mapping . NP_TYPE_TO_TENSOR_TYPE [ np . dtype ( 'int64' ) ] bias_name = "bias" + str ( kwargs [ "idx" ] ) tensor_node = onnx . helper . make_tensor_value_info ( bias_name , data_type , ( 1 , ) ) initializer . append ( onnx . helper . make_tensor ( name = bias_name , data_type = data_type , dims = ( 1 , ) , vals = [ 0 ] , raw = False , ) ) fcnode . append ( tensor_node ) node = onnx . helper . make_node ( "Gemm" , [ input_name , weights_name , bias_name ] , [ name ] , alpha = 1.0 , beta = 1.0 , transA = False , transB = True , name = name ) fcnode . append ( node ) return fcnode
short_docstring: Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
  and return the created node.
short_docstring_tokens: Map MXNet ' s FullyConnected operator attributes to onnx ' s Gemm operator and return the created node .
comment: """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
      and return the created node.
      """$SEP$# input (A, B, C) - C can be in place$SEP$# output