class_docstr: References
  ----------
  * https://github.com/onnx/onnx/blob/master/docs/Operators.md
class_docstr_tok: ["References","--","--","--","--","--","*","https",":","\/\/github.com\/onnx\/onnx\/blob\/master\/docs\/Operators.md"]
docstr: Parse a Reshape node.
docstr_tok: ["Parse","a","Reshape","node","."]
code: def _consume_reshape_nodes(self, node, next_nodes):
      """Parse a Reshape node."""
      if node.op_type != "Reshape":
          raise ValueError(
              f"{node.name} is a {node.op_type} node, only Reshape nodes can be used as starting points for consumption."
          )
      if len(node.input) != 2:
          raise ValueError(
              f"{node.name} input has {len(node.input)} dimensions, only nodes with 2 input dimensions can be used as starting points for consumption."
          )
      [in_0, in_1] = list(node.input)
      input_layer = self._node_map[in_0]
      new_shape = self._constants[in_1]
      output_size = (
          np.empty(input_layer.output_size).reshape(new_shape).shape
      )
      transformer = IndexMapper(
          input_layer.output_size, list(output_size)
      )
      self._node_map[node.output[0]] = (transformer, input_layer)
      return next_nodes
  
file_path: OMLT/src/omlt/io/onnx_parser.py
imports: import math
  import numpy
  from onnx import numpy_helper
  from omlt.neuralnet.layer import ConvLayer2D, DenseLayer, IndexMapper, InputLayer, PoolingLayer2D
  from omlt.neuralnet.network_definition import NetworkDefinition
label_desc: Use image node_id 8 for calling the NetworkParser obj's underlying member method code with example usage: obj._consume_reshape_nodes(node, next_nodes) and returns: next_nodes