Fix imports in docs

Signed-off-by: Felix Wang <wangfelix98@gmail.com>

docs/getting-started/concepts/feature-view.md

@@ -7,6 +7,8 @@ A feature view is an object that represents a logical group of time-series featu
 {% tabs %}
 {% tab title="driver_trips_feature_view.py" %}
 ```python
+from feast import BigQuerySource, FeatureView, Field, Float32, Int64
+
 driver_stats_fv = FeatureView(
     name="driver_activity",
     entities=["driver"],
@@ -39,6 +41,8 @@ If a feature view contains features that are not related to a specific entity, t
 {% tabs %}
 {% tab title="global_stats.py" %}
 ```python
+from feast import BigQuerySource, FeatureView, Field, Int64
+
 global_stats_fv = FeatureView(
     name="global_stats",
     entities=[],
@@ -70,6 +74,8 @@ It is suggested that you dynamically specify the new FeatureView name using `.wi
 {% tabs %}
 {% tab title="location_stats_feature_view.py" %}
 ```python
+from feast import BigQuerySource, Entity, FeatureView, Field, Int32, ValueType
+
 location = Entity(name="location", join_key="location_id", value_type=ValueType.INT64)
 
 location_stats_fv= FeatureView(
@@ -115,6 +121,8 @@ A feature is an individual measurable property. It is typically a property obser
 Features are defined as part of feature views. Since Feast does not transform data, a feature is essentially a schema that only contains a name and a type:
 
 ```python
+from feast import Field, Float32
+
 trips_today = Field(
     name="trips_today",
     dtype=Float32

docs/getting-started/concepts/point-in-time-joins.md

@@ -7,6 +7,8 @@ Feature values in Feast are modeled as time-series records. Below is an example
 The above table can be registered with Feast through the following feature view:
 
 ```python
+from feast import FeatureView, Field, FileSource, Float32, Int64
+
 driver_stats_fv = FeatureView(
     name="driver_hourly_stats",
     entities=["driver"],

docs/reference/data-sources/push.md

@@ -14,7 +14,7 @@ When using a PushSource as a stream source in the definition of a feature view,
 ### Defining a push source
 
 ```python
-from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature
+from feast import PushSource, ValueType, BigQuerySource, FeatureView, Feature, Field, Int64
 
 push_source = PushSource(
     name="push_source",

docs/reference/feature-repository.md

@@ -89,7 +89,7 @@ A feature repository can also contain one or more Python files that contain feat
 ```python
 from datetime import timedelta
 
-from feast import BigQuerySource, Entity, Feature, FeatureView, ValueType
+from feast import BigQuerySource, Entity, Feature, FeatureView, Field, Float32, String, ValueType
 
 driver_locations_source = BigQuerySource(
     table_ref="rh_prod.ride_hailing_co.drivers",

docs/reference/feature-repository/README.md

@@ -94,7 +94,7 @@ A feature repository can also contain one or more Python files that contain feat
 ```python
 from datetime import timedelta
 
-from feast import BigQuerySource, Entity, Feature, FeatureView, ValueType
+from feast import BigQuerySource, Entity, Feature, FeatureView, Field, Float32, String, ValueType
 
 driver_locations_source = BigQuerySource(
     table_ref="rh_prod.ride_hailing_co.drivers",

docs/tutorials/validating-historical-features.md

@@ -107,7 +107,7 @@ pyarrow.parquet.write_table(entities_2019_table, "entities.parquet")
 import pyarrow.parquet
 import pandas as pd
 
-from feast import Feature, FeatureView, Entity, FeatureStore
+from feast import Feature, FeatureView, Entity, FeatureStore, Field, Float64, Int64
 from feast.value_type import ValueType
 from feast.data_format import ParquetFormat
 from feast.on_demand_feature_view import on_demand_feature_view