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: Create an un-initialized dataset with schema provided by `fields`.
  
  Before this dataset can be used, it must be initialized, either by
  `init_empty` or `init_from_dataframe`.
  
  Args:
      fields: either a schema.Struct or a list of field names in a format
              compatible with the one described in schema.py.
      name: optional name to prepend to blobs that will store the data.
code: def __init__(self, fields, name=None):
      """Create an un-initialized dataset with schema provided by `fields`.
  
      Before this dataset can be used, it must be initialized, either by
      `init_empty` or `init_from_dataframe`.
  
      Args:
          fields: either a schema.Struct or a list of field names in a format
                  compatible with the one described in schema.py.
          name: optional name to prepend to blobs that will store the data.
      """
      assert isinstance(fields, list) or isinstance(
          fields, Struct
      ), "fields must be either a Struct or a list of raw field names."
      if isinstance(fields, list):
          fields = from_column_list(fields)
      self.schema = fields
      self.fields = fields.field_names()
      self.field_types = fields.field_types()
      self.name = name or "dataset"
      self.field_blobs = (
          fields.field_blobs() if fields.has_blobs() else None
      )
  
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 1 to create a new Dataset object  with example: obj = Dataset(fields, name)