class_docstr: Represents an in-memory dataset with fixed schema.
  
  Use this to store and iterate through datasets with complex schema that
  fit in memory.
  
  Iterating through entries of this dataset is very fast since the dataset
  is stored as a set of native Caffe2 tensors, thus no type conversion or
  deserialization is necessary.
docstr: Initialize the blobs for this dataset from a Pandas dataframe.
  
  Each column of the dataframe will be immediately fed into the current
  workspace, and the `net` will take this blobs as external inputs.
code: def init_from_dataframe(self, net, dataframe):
      """Initialize the blobs for this dataset from a Pandas dataframe.
  
      Each column of the dataframe will be immediately fed into the current
      workspace, and the `net` will take this blobs as external inputs.
      """
      assert len(self.fields) == len(dataframe.columns)
      self.field_blobs = [
          Const(net, dataframe.as_matrix([col]).flatten(), name=field)
          for col, field in enumerate(self.fields)
      ]
  
imports: from caffe2.python import core, workspace
  from caffe2.python.dataio import Reader, Writer
  from caffe2.python.schema import Struct, from_blob_list, from_column_list, InitEmptyRecord
  import numpy
label_desc: Use image node_id 4 for calling the Dataset obj's underlying member method code with example usage: obj.init_from_dataframe(net, dataframe) without return types